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000-834 - Object Oriented Analysis and Design - Part 2 (Design) - BrainDump Information

Vendor Name : IBM
Exam Code : 000-834
Exam Name : Object Oriented Analysis and Design - Part 2 (Design)
Questions and Answers : 180 Q & A
Updated On : October 19, 2018
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000-834 exam Dumps Source : Object Oriented Analysis and Design - Part 2 (Design)

Test Code : 000-834
Test Name : Object Oriented Analysis and Design - Part 2 (Design)
Vendor Name : IBM
Q&A : 180 Real Questions

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IBM IBM Object Oriented Analysis

Cloud buoys facts microservices -- for on-premises systems, too | killexams.com Real Questions and Pass4sure dumps

It will not be too startling to study that cloud architectures are coming to facts centers. however they are stuck on a company's premises, today's business architects need to get in on the action as records microservices evolve.

The cloud architectures they are bringing all the way down to earth come generally via containers that are in line with a microservices approach similar to old-time SOA -- which stands for carrier-oriented structure, not Sons of Anarchy, the outlaw biker club from the television exhibit of the identical name. records microservices are like SOA, simplest greater micro.

enhancing big data guide for containers became one of the crucial engineering to-do record gadgets noted with the aid of Hadoop vendors Cloudera and Hortonworks once they introduced plans to merge this month. That and a circulation to wider use of cloud object storage symbolize a big shift coming to massive records.

Kubernetes orchestration

whereas containers have been born as stateless vessels, work is underway to make them more stateful, with assist for persistence of facts in purposes. If successful, this could simplify the use of multiple for-intention analytics engines and frameworks -- believe Hive, Presto, Druid, TensorFlow and the like.

Used in conjunction with the increasingly widespread Kubernetes container orchestration expertise, records microservices will soon emerge as an option trend of records processing, even if on the cloud or off.

it's early, and there's loads of getting to know to do, as evidenced via Kubernetes classes that were frequently full finally month's Strata records conference in big apple. After researching comes building, an awful lot of which is vital earlier than the brand new structure can take root.

Enter Cloud deepest for statistics

For IBM and its customers, discovering a stability between on-premises and cloud statistics architectures is an ongoing quest. The business's full of life embody of both Hadoop and Spark has been stated earlier than, but IBM remains working to neatly kit these and other massive information mainstays for cloud and on-premises deployments.

near the conclusion of the Strata conference, we spoke with Rob Thomas, standard supervisor of IBM Analytics, as he organized for a panel discussion on the company's IBM Cloud deepest for data platform and the role it might play in bringing AI to fruition. The Cloud deepest for statistics kit comprises Db2, Db2 Warehouse and Db2 event shop, and it helps a lot of interfaces for information scientists and different predictive analytics clients, both on and off the cloud.

"if you developed a mannequin on deepest cloud, which you could set up it on public cloud. Or, if you developed it on public cloud, that you would be able to installation it on private cloud," Thomas talked about. The records instruction and governance tools are the equal even if the deployment is public or inner most, he added.

Thomas stated the company lately followed through on plans so as to add assist for MongoDB, EnterpriseDB's EDB Postgres and IBM information possibility supervisor to IBM Cloud inner most for statistics, and it is working to certify the platform to work with red Hat's OpenShift container application know-how.

Chart showing differences between monolithic and microservices Monolithic vs. microservices

IBM's normal facts-facet opponents are additionally seeking to connect facts analytics on premises with facts analytics in the cloud. Oracle's efforts middle on its Oracle Cloud at customer conducting. And Microsoft recently previewed a version of SQL Server 2019 that may deliver the Azure types of Spark and Hadoop down into the statistics center.

IBM has tremendous cloud development and data alliances with purple Hat and Hortonworks, both of which contact on statistics microservices. both organizations combined with IBM simply before Strata to launch an Open Hybrid architecture Initiative. The assignment's goal is to meld Kubernetes, containers and Hadoop workloads, and there's plenty work to be performed in that regard, Thomas mentioned.

Remaking the monolithic

After Strata, we checked in with Tom Phelan, co-founder and chief architect at BlueData software, maker of the utility platform BlueData Epic, which is brief for Elastic inner most rapid Clusters. He noted he preferred the goals of the Open Hybrid architecture Initiative, but delivered that there must be more work earlier than it might circulation ahead.

"a huge problem is that, when Hadoop became written, it become written as a monolithic structure," Phelan noted.

that's the case despite the fact that Hadoop will also be damaged down into features such because the identify node, the YARN aid manager, Hive, Hadoop distributed File gadget functions and the like.

When Hadoop became written, it became written as a monolithic architecture. Tom Phelanchief architect, BlueData

"None of those are microservice-architected," Phelan talked about. "those services ought to be began in a undeniable order. They have to have interaction with each different service in a very selected method. there is, for instance, no method to restart a type of functions and not have it have an impact on other features that are running."

For its half, BlueData this month released KubeDirector, an open source mission for establishing stateful application clusters on Kubernetes.

New scheme coming?

no matter if information strikes to the cloud or now not, the impact of cloud architecture looms as enormous.

"organizations definitely have a lot of on-premises records going into the cloud, however not all of the statistics goes to the cloud," talked about Forrester analyst Noel Yuhanna. "every so often, when the cloud invoice comes in, it may well be huge -- possibly more than on premises."

moves such as the Open Hybrid structure Initiative handle the deserve to harmonize views within the two domains, in line with Yuhanna, and set the stage for standard management guidelines for each.

"You desire an structure that can manipulate both cloud and on-premises information with commonplace guidelines," he introduced.

back when SOA walked the earth, it led to a remodeling -- or rewrapping -- of legacy techniques. New records microservices may make latest big information methods similar to legacy programs. that would be nothing more than development.

it's an excellent wager that, as hybrid records processing becomes extra true, records could be shuttling from here to there and lower back. And, of direction, YANA -- Yet an extra New structure -- could be waiting in the wings.


Managing to the next Century - The 5 large things For Agile Transitions | killexams.com Real Questions and Pass4sure dumps

Key Takeaways
  • a hit transformation wants strong management to aid and give protection to agile way of life
  • help groups and stakeholders to self-organize
  • control your portfolio with effects (now not Output)
  • Systematically get rid of sources of waste and prolong confronted by way of Agile groups
  • Measure and increase price delivered with frequent comments (investigate cross-check and Adapt)
  • moving to Agile is difficult, but necessary

    As we flow into the digital age, businesses more and more are looking to be greater agile. possibly it is prompted by way of loss; loss of customers, employees, market share or brand cost. It additionally may be pushed by a need to “keep up with the Joneses” and the desire to be as cool as Amazon or Netflix. Motivation no longer withstanding agile transitions are usual location with many companies trying to develop into “more agile”.

    despite the fact, for a lot of corporations, the journey to becoming “more agile” is suffering from false starts, unhappy stakeholders and aggravated and perplexed leaders. becoming agile is tricky, now not simply on account of agility, but usually because any trade is difficult for organizations to embark upon. to cite Niccolo Machiavelli:

    there's nothing more elaborate to absorb hand, more perilous to conduct, or greater doubtful in its success, than to take the lead in the introduction of a brand new order of things. - The Prince (1532)

    And the stream to agile is a new order of issues, a a lot greater basic exchange than the adoption of a brand new methodology or system framework. Carlota Perez world noted economist describes the change as ordinary-feel response to the digital age.

    no one these days would propose a centralized, inflexible, top-down organizational constitution, where you cannot talk throughout services except via your bosses. youngsters, that was exactly what Alfred Sloan install at conventional Motors, to a great skills at his time. With these days’s communications and flexible applied sciences, agile creative networks make more sense and lead to lots extra productiveness.

    Perez goes on to argue that the previous age focused on mass creation. Which by using its very nature is diverse from digital construction. Mass product specializes in reducing the unit cost, and lengthening excellent by procedure optimization, conformity and standardization. Digital in spite of this, focuses on the unit measurement of one, the place every little thing is exciting in terms of the issue, solution and ultimate price circulate. but how many groups nevertheless manage their digital products with the identical mass construction paradigm with useful resource optimizations, standardized system and specialism of labor? The agile age requires a different method to outcomes, work, groups and management.

    a focus on outcomes – taking ownership of the company value

    instead of a spotlight on getting work completed within the most cost-effective, constant, chance hostile method, agile teams be aware of supplying cost in the most constructive way. They focus now not on the work, but in its place the effects they are attempting to carry. Scrum, the area’s most prevalent agile framework, is empirical in approach. for that reason, issues are damaged down into small experiments with regular commentary which may end up in changes. Smaller batches of work coupled with the should concentrate on value capability:

  • possession becomes a right priority - In most ordinary corporations, selections are made in a gradual, consensus driven method. documents are used to store the details and are signed off to ensure that each person knows who became involved. exchange is treated as an issue with expensive administration techniques, review committees and escalation procedures. Agility requires decisions to be made, and work to be delivered permitting comments and improvement. That requires ownership.
  • value must be certainly understood and measured, which looks like an easy requirement - The reality is that much work does not have a clear connection to value. dimension is additionally very complex as agencies wrestle with visibility, transparency and possession.
  • teams must be aligned to consumers – often teams have a poor realizing of their relationship with a customer and despite the fact that they've the improvement of knowing the customer they neither are aligned to them or can get access to them. shoppers are managed by the front conclusion of the enterprise, income, advertising and assist. Product development has to work with these groups to get access and comments.
  • regular understanding requires customary observation – For digital items, that capability offering them to valued clientele for use. delivering frequently has massive implications on every part of the building procedure from how requirements are managed, to how the product is verified. How the underlying application is architected also alterations to support smaller more everyday alterations. For non-digital or hybrid items the deserve to accumulate established remarks also alterations the style work is executed with extra of a spotlight on accepted evaluate, simulation and trying out.  
  • Work is not owned by a supervisor

    within the new agile world, it is neither feasible to inform americans to do a specific task or plan on the equal stage of breadth or depth. Work is described, managed and done by using empowered teams who are not concentrated on the task, but as a substitute the effect they are trying to obtain. first-rate including technical debt is handled in the same means that price is handled allowing the team and the business to make express, clear decisions on exchange-offs. but relocating faraway from ordinary managed work to a extra agile approach requires greater than managers stepping away however requires corporations to have:

  • readability of accountability and possession – In most up to date groups all and sundry is liable, however no one is in charge. In an agile organization, it is clear that the group is liable for the outcomes, the company for deciding where to center of attention the group when it comes to value and the organization dependable for constructing the ambiance for the team to be a hit. by having clear responsibilities and enabling groups to make choices agile groups can right now cut away a lot of the waste associated with traditional correct down administration techniques.
  • management concentrated on constructing the appropriate ambiance – Many managers get their place as a result of they are decent at doing the work. That leads to a lifestyle of “parental-ism” with managers telling individuals what to do. For essential work this can be helpful, but when work becomes complex and system variable it is awfully intricate requiring the manager to be all the time there. The management function hence has to alternate to coaching, and servant management assisting the team contend with the work and center of attention on the effects.
  • projects replaced by using products – for many groups, work is carried out through initiatives, and at anyone time there are heaps of projects being run. each task has funding, components and loads of documentation. people work on distinct projects and groups are created and destroyed often. Agility doesn't drive a firm not to do projects, however does encourage a greater focused, team centric strategy to work. with the aid of cutting back tasks and relocating to a product or customer alignment, groups can more suitable focus on outcomes. further benefits are more straightforward planning cycles, reduced complexity of verbal exchange and decreasing of the dreaded context switching difficulty. projects will nonetheless exist for initiatives that move diverse consumer / product / result boundaries, however they usually are not the norm. 
  • groups are precise

    at the very coronary heart of the agile corporation is a set of groups, self-organized and empowered to make choices. they have the entire correct expertise to deliver cost and are supported via a company that fills in any gaps and helps them to get more desirable.  At scale that skill groups of teams and the adoption of practices to be sure that dependencies are with no trouble managed. but unlike on this planet of mass production, agile groups do not improvement from “throwing lots of our bodies at a problem”, and instead encourage leaner, “smarter” organizational fashions. companies may wish to alternate their approach to teams by means of:

  • concentrating on constructing lengthy lived teams – teams don't seem to be a new thought for agencies, in fact for many they like the conception so a whole lot they put americans on multiple groups. Agile organizations motivate a extra focused, people centric mannequin with teams having an id and consistent membership. That capacity that work is dropped at the team as opposed to the crew kinds across the work. this can require a metamorphosis in considering and procedure for a lot of mission administration agencies.  
  • separating of labor management from americans administration – It is awfully complicated to be transparent when the grownup who reviews you, determines your bonus and decides on promotions is working with you on an everyday basis. by way of having a clear separation of labor management and americans management on agile groups encourages transparency and openness. Work is managed by way of the team, individuals building and administration is both managed via some sort of neighborhood or guild, or by way of more normal management.
  • Incrementally evolving the architecture in opposition t valued clientele – for a lot of agencies, people are aligned to applications and people applications are reused in numerous consumer or price contexts. That results in complexity with every little thing affecting everything. modern, agile enterprise architectures focal point on optimizing for agility. That can also imply that some things are duplicated and that the whole cost of possession for someone software might possibly be greater, but subsequently the price of responding to the market outweighs that can charge. Optimization strikes when it comes to place in the stack because it has executed outdated with adjustments to the cost of disk area or processing.
  • diversity concerns – To reply to advanced issues requires skilled teams with numerous diverse views and expertise. diversity of opinion, thought and answer are essential for offering cost. however for a lot of businesses team boundaries re-implement departmental, gadget and adventure boundaries. For agile to work, and for businesses to bring splendid consumer options it is important to construct diverse teams that challenge the popularity quo, seem and consider distinctive.
  • Introducing the 5 challenges of Agile Transition

    moving to the “new order” is not handy, and a lot of companies have begun the journey with the adoption of Scrum, and are scaling frameworks comparable to less, protected and Nexus, and organizational fashions just like the Spotify. sadly, there is not any single silver bullet for “remodeling” your firm into an agile, digital native business. looking at organizational frameworks and scaling fashions are extraordinary tools and only 1 piece of the puzzle, because the circulation requires a more holistic strategy to agility. It also can't be concept of as changing one methodology with an additional, however as an alternative something more basic to the way the corporation responds to the market and delivers price. groups may still concentrate on 5 wide challenges.

  • support and protect agile way of life with robust leadership
  • assist groups and stakeholders to self-prepare
  • manipulate your portfolio with outcomes (not output)
  • Systematically remove sources of waste and delay confronted via Agile groups
  • Measure and enrich value delivered with common feedback (inspect and adapt)
  • guide and give protection to agile tradition with effective leadership

    The tradition of the corporation is described by using the norms enacted by using the corporation in how they do everything. leadership defines and re-enforces these norms in the moves and words. Having leaders that embody the values of agility and strengthen its principles is essential for any transition. as an example, agility requires a continuous approach to getting to know and realizing, but when leaders focal point on errors and apportioning blame, then it is probably going that any empirical method will no longer work.

    assist groups and stakeholders to self-arrange

    developing an ambiance the place groups self-arrange and are empowered to make selections that deliver value is the foundation for agility.  regrettably, just telling groups to be empowered and self-organize is not an ideal way to introduce these ideas. as an alternative exchange has to be delivered incrementally and practices similar to Delegation Board from administration 3.0 can aid slowly circulate teams to taking the lead.  

    manage your portfolio with outcomes (no longer output)

    Agility like anything is simply as good because the influence you're focused on. by way of constructing an agile business it is focused on client outcomes instead of work you not only permit greater alignment, but you also pressure a transparent imaginative and prescient and aim. average companies have at all times concentrated on the gadget, with most effective small ingredients of the organization being connected to the client and the consequences they are seeking for. contemporary, agile organisations make outcomes within the context of clients’ needs front and center and make each person chargeable for them.

    Systematically eradicate sources of waste and prolong confronted by way of Agile groups

    Waste is the enemy now not of efficiency but of price. Waste to agile groups is the rest that stops them from offering cost and improving. Waste corresponding to hand-offs to exterior departments to waste in time spent in evaluate conferences. To develop into extra agile every thing is on the “waste removal” agenda and nothing is “set in stone”. That capacity that corporations akin to finance, audit and compliance deserve to be part of the exchange enabling them to be review current strategies as a way to exchange. Planning is often a neighborhood the place waste is current with long term planning cycles and alter management tactics taking helpful time far from key stakeholders and birth teams. In Scrum, waste is managed all through with the aid of making it transparent in the day by day Scrum and alternatives for improvement being discussed in dash assessment and Retrospective. but outdoor of any particular framework agile teams study everything with a watch to be trained and enrich. They even have the courage to problem the reputation quo.

    Measure and enhance price delivered with conventional comments (check out and Adapt)

    if you have been going to measure one thing to verify if an business is agile it will be how often they get their shoppers, stakeholders and teams to overview the delivered objects. Getting real records on the use on what you have got delivered, coupled with a need to create the simplest element to get comments are cornerstones to agility. noted agile corporation consumer centric company Amazon famously can provide application each eleven.6 seconds. not because it wishes so as to add greater features, but since it desires to are trying new ideas with its valued clientele and get feedback.

    summary

    It seems ironic, however many groups think of a flow to agile as a hill to be climbed the usage of natural methods. construct a plan, discover the supplies and then execute. however fitting an agile business is a whole lot more than adopting a selected technique framework or altering job titles. it's a primary trade to the manner in which you respond to the market. Organizational models, approaches, tools and even the underlying items will change often in response the wants of the consumer, market and stakeholders. An agile commercial enterprise is at its coronary heart a customer-centric discovering corporation. Peter Senge in his booklet the 5 discipline, The artwork and practice of a discovering organization describes the want for an organization to fitting concentrated on studying.

    “The most effective sustainable aggressive talents is a firm's means to be trained faster than the competitors”.

    sooner or later, by using concentrating on the 5 challenges of agile management, self-company, becoming customer effect centric, waste antagonistic and frequently providing learning the culture of the firm, groups will become more focused on gaining knowledge of and will develop into more agile.  

    concerning the writer

    Dave West is the Product proprietor and CEO at Scrum.org. he is a typical keynote at principal trade conferences and is a widely posted author of articles and research studies. He is also the co-writer of two books, The Nexus Framework For Scaling Scrum and Head First Object-Oriented evaluation and Design. He led the development of the Rational Unified system (RUP) for IBM/Rational. After IBM/Rational, West returned to consulting and managed Ivar Jacobson Consulting for North america. Then as VP, research director Forrester analysis, he ran the utility construction and delivery observe. prior to joining Scrum.org he changed into Chief Product Officer at Tasktop where he changed into chargeable for product administration, engineering and structure. As a member of the business’s government management crew was additionally instrumental in growing to be Tasktop from a services company right into a VC backed product company with a crew of very nearly a hundred.


    IoT analytics e book: What to are expecting from information superhighway of issues data | killexams.com Real Questions and Pass4sure dumps

    The growth of the information superhighway of things (IoT) is having a big impact on loads of areas inside enterprise IT, and information analytics is one among them.

    corporations are gathering big volumes of tips from every kind of linked of objects, such as information about how consumers are the use of definite items, the efficiency of company belongings, and the environmental situations wherein programs operate. by making use of advanced analytics to those incoming streams of records, companies can profit new insights that may aid them make greater advised selections about which movements to take. And with companies placing IoT sensors on further and further objects, the volumes of incoming facts will proceed to develop.

    "Sensor-based computing is a core vogue in digital transformation," says Maureen Fleming, an analyst at analysis company IDC. "Operational intelligence the usage of situation-based mostly monitoring assures businesses about the health of sensor-connected contraptions, machines and programs. counting on the use case, making use of laptop gaining knowledge of [ML] to sensor facts is geared toward predicting likelihood of outages, propensity to buy, health complications, and so forth."

    making use of ML to sensor information in mixture with statistics from enterprise functions can additionally fundamentally trade how a company works, by using predicting issues with meeting provider-stage agreements on services for purchasers or logistics complications within a give chain, Fleming says.

    IoT "is using the blending of the digital and actual worlds," says Brian Hopkins, vice chairman and most important analyst at Forrester research. "almost all businesses want true-time statistics from the actual world to take the subsequent step in their quest for insights that deliver aggressive advantage."

    Forrester sees three primary scenarios for gaining insight via analytics. One is perception in regards to the smart connected items themselves. an extra is perception about how linked issues work efficiently together, that may assist groups increase techniques that contain actual property. And the third is insight about things and individuals that come from the IoT statistics of enterprise companions comparable to suppliers.

    for a lot of enterprises, the present facts analytics infrastructure will not adequately handle the expected raises in quantity generated by the IoT, however. they will should alter their IT environments to make them greater “IoT-equipped.”

    “IoT is growing an exceptional quantity of records in the business when it comes to each extent and speed,” says Mark Hung, analysis vice chairman at research firm Gartner. “to be able to extract value out of this statistics, the commercial enterprise’s statistics analytics structure must be revamped.”

    For businesses to act on IoT statistics in a well timed manner, streaming or true-time analytics is commonly required, Hung says. The should comprise new analytics strategies akin to streaming analytics and new infrastructure corresponding to side gateways areas new architectural requirements on the current IT infrastructure, he says.

    Analytics for IoT has some interesting requirements compared with analytics for different kinds of data. This includes data structure, records richness, time sensitivity, the place the records is saved and how lengthy it is saved.

    “the key analytics want is to close the gap between statistics technology in the actual world and the need for action either within the actual or digital world,” Hopkins says. “This inevitably capability pushing some analytics common sense to the aspect




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    Object Oriented Analysis and Design - Part 2 (Design)

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    Service Stubbing With JMeter and Docker | killexams.com real questions and Pass4sure dumps

    In this article series about Docker (with JMeter, Distributed, IBM MQ and Cloud) I described how Docker adoption simplifies the performance testing process with Apache JMeter™ and makes it more reliable. These advantages are obtained thanks to the flexibility of container virtualization, that is a key feature of Docker.

    In this article, I will present another Docker applicability to performance testing: service stubbing with Docker containers. By replicating dependencies through stubbed services, testers can overcome the challenges dependency systems create to their test procedure: fragility, instability, and more. In this article I will show how to do this with Docker, and with JMeter to replicate the service. Then, you can use JMeter as the testing tool.

    You might be surprised how stubbing can be mixed with Docker, but in the article I will introduce the necessary concepts and some practical examples. Let's get started.

    Service Stubbing for Performance Testing: An Introduction

    With the increasing complexity of testing projects, more and more testing flows of the System Under Test are impacted by dependency system(s). When I say "dependency system" I am referring to:

  • A legacy system not affected by current development
  • A third party service that belongs to another organization
  • A system developed by your organization with an unclear roadmap
  • The presence of dependency systems brings every manual or automatic testing project in a situation like the one described in the picture below.

  • Testing Actors - in the diagram I'm showing a testing project that stress tests the System Under Test to validate its quality in different ways, both manually and with automation.
  • System Under Test (SUT) - a complex architecture composed of heterogeneous services and infrastructure that implement a business requirement.
  • Additional Systems - these services collaborate with the final application but are out of the project management scope. Possibly, some of their requirements are unclear. These are the dependencies.
  • In the picture above it is important to focus on the arrows too. These arrows describe the systems' perimeters and the interfaces used to exchange messages/information:

  • The red arrow symbolizes the test specification that is run in the testing tool (JMeter in this case).
  • The grey arrow symbolizes that there is a relationship of functionality that the tester knows little about. This could potentially waste your time, if you understand too late that dependencies broke your test logic. So, plan in advance, find out about the dependencies, stub them, develop your plan and proceed with the test.
  • The presence of dependency systems can produce a series of unpleasant effects throughout the testing project (both for manual and automatic testing):

  • Fragility - the test becomes more fragile with the increasing number of uncontrolled dependencies, because you might get a failed test when the problem is with the dependency and not the SUT, rendering the test unreliable.
  • Instability - dependencies can bring the system down when the SUT is up, affecting the test result without a clear correlation to the SUT.
  • Time consuming - setting up test data may take days/weeks and it's potentially error-prone.
  • Unavailability - the testing team may spend time waiting for the availability of dependencies systems.
  • Frustration - the human aspect of the matter:
  • Testers - you might feel frustrated because you cannot do your job effectively
  • Managers - you might feel frustrated because your team wastes time because its work is blocked
  • To eliminate, or highly reduce, these effects, one solution is to decouple the test procedure executed on the SUT from the uncontrolled dependencies. One way to decouple is adopting stubbed interfaces, with the goal of replicating the behaviour of dependencies. In this manner, the testing procedure does not encounter unpredictable behaviour as it would with the original dependencies system. Thus, the test perimeter will now include the stub services because they are part of testing process. So, we will add a new endpoint to the testing perimeter in our diagram.

    To create stubbed interfaces in out test plan we need to:

  • Add a new system called a stub service that replicates the interface to be stubbed
  • Define which calls must be stubbed according to the planned test procedure
  • Understand that the stub is part of the test suite and is included in the test perimeter
  • In the picture above I added a new entity, a stub service, into the testing perimeter. The stub service acts like the original dependency when queried, only it answers with specific messages coherent to the testing scopes. A stub can reproduce both functional characteristics, as well as non-functional characteristics, such as response times or slow connections.

    Let's understand the flow of the message/information exchange:

  • The red arrow has a similar function to the previous picture with a little difference: the testing team has produced a precondition with the stub and the SUT
  • The green arrow - is the stubbed interface that reproduces a subset of all possible requests/responses from the real dependencies system. How do you decide which requests/responses you should stub? This depends on two factors:
  • Testing coverage - the testing team must assure the quality of the SUT. This goal is achieved via test coverage. More test coverage requires more complex stubbing.
  • Stub setup effort - The testing team is responsible for setting up stubbed interfaces and maintaining them. So, resources must be allocated accordingly. This is important to understand because some stubs can be complicated to maintain and the effort can become hugely unexpected compared to the original testing plan.
  • Concluding this section: stubbing might be the key to the success of a complex testing project.

    Virtualization as a Shortcut for Service Stubbing

    Developing a stub service require significant planning and implementation efforts. Sometimes, project members adopt quick and dirty solutions to speed up the software lifecycle activities. They assume they will need a short period of stub usage (e.g. developing one feature, testing one feature). A quick and dirty solution is attractive but it tends to:

  • Have a short lifecycle without any guarantee of reproducibility over time.
  • Be used by only one environment, typically the one where it is developed.
  • Simple stub solutions are based on hard coded and/or parameterized data. There are more complex stub services, based on a data model solution. A setup of stub services becomes more complex and sometimes requires proper training, but obtaining stubbed services is more recommended for a project with a wider ambition.

    In this article, we will describe how to implement a stub service using a virtualization service. Obtaining a virtual stub service grants stub isolation from the rest of the environment. Moreover, a virtual stub service enables migrating the stub service over networks in cases when:

  • Departments require sharing stubs so they can collaborate and work with the same references during subsystem development.
  • It's required to improve the stub service infrastructure (e.g. computational load and bandwidth) during testing (e.g. performance testing).
  • The virtual stub service can be created using a virtualization layer. We will use a virtualization based on Docker, because Docker makes it possible to execute a stub in an isolated context that reproduces only the required features.

    Docker-based Stub Services

    Docker offers the possibility to setup a stub service easily and with a high level of reliability. As we described in previous articles, the possibility to reproduce previously configured behaviour is quite instantaneous in Docker. Moreover, the virtualization layer creates an isolation that executes the stubbed interfaces as an external service.

    Now it's time for some examples that will teach us how to do it. I will present two containers, and a stub specific service interface for each one. The proposed examples are my own: they use self assigned requirements without any correlation to a commercial situation.

    SUT: Big Data, Stub: Data Source Based on S3 (Example 1)

    In this example, we are required to set up a stub for a data source based on an S3 bucket that generates continuous data. This data can be characterized according to velocity, variety, and volume. A potential SUT target for this stub is a Big Data application.

    Our test requirements for this example stub are:

  • The stub generates the defined kB size of random chars as a S3 object
  • The stub uploads a new S3 object periodically
  • The stub sends the generated S3 object to assigned bucket
  • By analyzing these requirements we can extract parameters that we add to the stub implementation to customize our stub behavior at boot time:

  • kbSize - the kB size of every object generated
  • sheetAtMinute - the indication of how many objects are uploaded each time
  • bucket - describes the target bucket name
  • With the defined interface parameters and the expected output behaviour, we can proceed with the stub architecture definition. We will adopt Docker as stub resource manager and split our stub into three sub-systems:

  • A Minio server container to expose the S3 interface to the SUT
  • A JMeter container that runs in non-GUI mode to generate a data stream
  • A network configured via Docker so containers can communicate and the SUT can see the bucket externally.
  • The final stub design is presented in the picture above. This design assumes that the stub is queried by the testing procedure to verify testing goals (e.g. check a report file in the bucket).

    Stub Code

    As always, here is the code of the described application. You can also find it on GitHub. For this stub see the example code located in the "s3-stub" folder.

    $DO_TOKEN="your-digitalocean-token" $MACHINE_NAME="my-machine" docker-machine create ` --driver=digitalocean ` --digitalocean-access-token=$DO_TOKEN ` --digitalocean-size=1gb ` --digitalocean-region=ams3 ` --digitalocean-private-networking=true ` --digitalocean-image=ubuntu-16-04-x64 ` $MACHINE_NAME $MACHINE_IP = ((docker-machine ip $MACHINE_NAME) | Out-String).trim() & docker-machine env $MACHINE_NAME --shell powershell | Invoke-Expression

    This PowerShell code creates a Docker machine while taking into account hardware requirements and the machine location (this can be another requirement for our stub). In the end, the code extracts the real machine IP address and configures its current shell to execute the next Docker command on the newly created Docker machine.

    Please note: Service stubbing often requires simulating a service located over a network (e.g. local network, corporate network, worldwide), so we must be prepared for a situation where the stub location can be "rented" on the Cloud.

    $SUB_NET="172.18.0.0/16" $STUB_NET="mydummynet" docker network create ` --subnet=$SUB_NET $STUB_NET

    This code creates a custom Docker network where the containers our stub is composed of will be placed. Please note: the entire network and the related containers are invoked on the local machine, but executed on a remote machine thanks to the "env" command.

    $accessKey="AKIAIOSFODNN7EXAMPLE" $secretKey="wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY" docker volume create s3data docker volume create s3conf docker run ` -p 9000:9000 ` --detach ` --name minio ` --env "MINIO_ACCESS_KEY=$accessKey" ` --env "MINIO_SECRET_KEY=$secretKey" ` --net $STUB_NET ` -v s3data:/data ` -v s3conf:/root/.minio ` minio/minio server /data

    This code sets up a Minio server container, which takes the role of the S3 interface for our stub. The code steps are executed in the following order:

  • Defining access/secret keys as required to interact with the S3 bucket.
  • Creating two Docker volumes that backup the Minio server configuration and the data, in case of a container crash. It is useful to post crash analyses.
  • Creating a container in detached mode with the defined configuration.
  • $MACHINE_KEY="${env:HOMEPATH}/.docker/machine/machines/$MACHINE_NAME/id_rsa" # this command provides necessary jar for Minio scp -o StrictHostKeyChecking=accept-new ` -i $MACHINE_KEY ` ../../minio-4.0.2-all.jar root@${MACHINE_IP}:~ scp -o StrictHostKeyChecking=accept-new ` -i $MACHINE_KEY ` ./script.jmx root@${MACHINE_IP}:~ docker run ` --detach ` --name jmeter ` --env "JMETER_USER_CLASSPATH=/root/" ` --env "MINIO_ACCESS_KEY=$accessKey" ` --env "MINIO_SECRET_KEY=$secretKey" ` --volume /root/:/root/ ` --net $STUB_NET ` vmarrazzo/jmeter ` -n -t /root/script.jmx

    In the last part of the PowerShell code, we launch a JMeter container in detached mode. This container executes the logic of periodically uploading a new S3 object to the bucket. Before container execution it's necessary to upload the following on the Docker machine via the scp command:

  • minio-4.0.2-all.jar - a necessary library for executing Groovy code in a JMeter script
  • script.jmx - the JMeter script with the stub logic (e.g. scheduling and object creation)
  • The picture above shows the test plan structure of the JMeter script used for stub. This script

  • Takes stub arguments from the Docker run command.
  • Establishes a connection with the Minio server via its Java API.
  • Periodically iterates the generation of a random S3 object and its upload on a defined bucket.
  • For implementation details, see the Groovy code committed on GitHub. When stub containers are running, it is possible to navigate over the bucket via the web by using the Docker machine IP address at port 9000.

    You can now create your JMeter script for load testing the application.

    SUT: Application, Stub: IBM MQ Endpoint (Example 2)

    This example covers messaging-oriented middleware by showing how to stub an IBM MQ based application. The key features that characterize this middleware are asynchronicity, routing, and transformation. Applications like IBM MQ are highly adopted where it is mandatory to facilitate message exchange with a standard API.

    The final stub can simulate a remote endpoint that the SUT occasionally queries during the test project. This situation creates a complicated test and stubbing can be a good solution in a reasonable amount of time.

    As already seen in the previous example, we have test requirements that define how the stub works:

  • IBM MQ version and its configuration
  • Sample message request received by the SUT
  • Sample message response to be sent to the SUT
  • Analyzing these requirements, we need to involve additional colleagues to resolve the knowledge gap necessary to setup our stub. In particular:

  • sysadmin - to identify the software version and its configuration (e.g. TLS config)
  • developer - to obtain a sample version of messages, like these:
  • Image title

    After an inquiry session and a series of "Elementary, my dear Watson" we can adopt a stub architecture that has three subsystems (again):

  • IBM MQ container to expose MQ interfaces to SUT
  • Consumer/Producer request/response logic into ad hoc container based on custom Java code
  • Network configured via Docker, so the containers can communicate and the SUT can see the bucket externally
  • The final stub design is presented in the picture above. This design assumes that the stub is probably queried by the testing procedure to verify the testing goals (e.g. check queue stats).

    Stub Code

    Before describing the code it's necessary to provide a technical description of the stub and its containers. In detail:

  • IBM MQ container is based on the image "vmarrazzo/wmq" already seen in the previous article. It is an IBM MQ V9 with two queue managers that differ to channel encryption with/without TLS security. In this example we suppose that the SUT communicates with TLS security and we have the encryption key.
  • The "java-stub" is a custom project developed in Java, which interacts IBM MQ to the stubs expected MQ behavior. This project uses Apache Maven as a build toolchain so it is useful to reuse Maven for our Docker container. The Maven image is useful for executing Maven goals in the isolated environment based on the Docker container.
  • Here is the code of the described application. The code described in this paper is available on GitHub. For this stub see the example code located in the folder "ibmmq-stub".

    $MACHINE_KEY="${env:HOMEPATH}/.docker/machine/machines/$MACHINE_NAME/id_rsa" # this command uploads necessary TLS for IBM MQ scp -o StrictHostKeyChecking=accept-new ` -i $MACHINE_KEY ` ./my-cert.pfx root@${MACHINE_IP}:~ # this command uploads necessary TLS for Java Stub scp -o StrictHostKeyChecking=accept-new ` -i $MACHINE_KEY ` ./my-cert.jks root@${MACHINE_IP}:~ # this command uploads Java Stub source code scp -o StrictHostKeyChecking=accept-new ` -i $MACHINE_KEY ` -r ./java-stub root@${MACHINE_IP}:~ $destPath="~/java-stub/localrepository/com/ibm/com.ibm.mq.allclient/9.0.4.0" # this command provides jar for IBM MQ scp -o StrictHostKeyChecking=accept-new ` -i $MACHINE_KEY ` <your_path>/com.ibm.mq.allclient-9.0.4.0.jar ` root@${MACHINE_IP}: $destPath

    The above code is a mandatory precondition for container execution and it includes:

  • A keystore in PKCS #12 archive format (pfx) for a TLS channel on IBM MQ
  • Java keystore (jks) required by Java stub to handle IBM MQ messages
  • The "java-stub" source code project that implements stub logic
  • The Java driver for the IBM MQ required by "java-stub" code
  • $QM_VOLUME_NAME="qm1data" $ks_file="my-cert.pfx" $ks_pass="changeit" docker volume create $QM_VOLUME_NAME docker run ` --env MQ_TLS_KEYSTORE=/var/ks/$ks_file ` --env MQ_TLS_PASSPHRASE=$ks_pass ` --publish 1415:1415 ` --publish 9443:9443 ` --name wmq ` --detach ` --net $STUB_NET ` --volume ${QM_VOLUME_NAME}:/mnt/mqm ` --volume /root/:/var/ks ` vmarrazzo/wmq

    This code section is quite similar to the one already seen in IBM MQ in the previous Docker article. So after this Docker runs the command, a container is run with an IBM MQ instance inside with a TLS channel on the 1415 socket.

    docker volume create --name maven-repo docker run ` -it --rm ` --net $STUB_NET ` -v /root/:/opt/maven ` -v maven-repo:/root/.m2 ` -w /opt/maven ` maven:3.5-jdk-10 ` mvn clean compile exec:java -DIBMMQ_HOST=wmq

    This is the last PowerShell section and it is related to the execution of a Maven container. This Docker runs a command that executes the stub main class by using a project file descriptor called a pom file. In addition:

  • The container is executed in a created network where IBM MQ is running
  • The container receives the IBM MQ container name as input, so the Docker network resolves this name with the IP address container
  • After the execution of Docker run commands, the resulting stub is ready to be engaged by the SUT by producing the following actions:

  • Listening for incoming messages on the IBM MQ queue to be parsed
  • After correctly receiving the message, it generates the answer message using the data carried to the request message.
  • Based on this current article and a previous one, the reader can implement a load script to test the final stub behavior.

    Conclusion

    In this article, I showed another way to use Docker in a performance project, by using service stubbing based on Docker virtualization. I also described examples that used various Docker containers we started working with in previous articles. These obtained stub services demonstrate container flexibility in different contexts (e.g. Maven container is for developing purpose).

    After setting up your system, you can run performance tests on it with BlazeMeter. Create your JMeter script, scale it in BlazeMeter, share your tests with colleagues and managers, and analyze results.

    To try out BlazeMeter, request a live demo from one of our performance engineers.


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    The Improbable Origins of PowerPoint | killexams.com real questions and Pass4sure dumps

    img Illustration: Jude Buffum

    Walking into the hall to deliver the speech was a “daunting experience,” the speaker later recalled, but “we had projectors and all sorts of technology to help us make the case.” The technology in question was PowerPoint, the presentation software produced by Microsoft. The speaker was Colin Powell, then the U.S. Secretary of State.

    Powell’s 45 slides displayed snippets of text, and some were adorned with photos or maps. A few even had embedded video clips. During the 75-⁠minute speech, the tech worked perfectly. Years later, Powell would recall, “When I was through, I felt pretty good about it.”

    The aim of his speech, before the United Nations Security Council on 5 February 2003, was to argue the Bush administration’s final case for war with Iraq in a “powerful way.” In that, he succeeded. While the president had already decided to go to war, Powell’s speech—inseparable from what would become one of the most famous PowerPoint presentations of all time—did nothing to derail the plan. The following month, the United States, United Kingdom, Australia, and Poland launched their invasion.

    imgimg Top: U.S. Department of State/Getty Images; Bottom: Thomas Monaster/NY Daily News/Getty Images

    “Evasion and Deception”: In a 2003 speech to the United Nations Security Council, then U.S. Secretary of State Colin Powell made the case for going to war with Iraq. The accompanying PowerPoint slides included satellite imagery [top] that Powell said showed secret work on chemical and biological weapons.

    Powell’s speech dramatized how PowerPoint had become, by 2003, a nearly inescapable tool of communication and persuasion in much of the world. Since then, its domination has only become more complete. The same tool used by U.S. State Department and CIA officials to pivot an international coalition toward war is also used by schoolchildren to give classroom reports on planets, penguins, and poets. Microsoft rightly boasts of 1.2 billion copies of PowerPoint at large—one copy for every seven people on earth. In any given month, approximately 200 million of these copies are used, and although nobody’s really counting, our cumulative generation of PowerPoint slides surely reaches well into the billions. So profound is PowerPoint’s influence that prominent figures have decried the software’s effects on thinking itself. Edward Tufte, the guru of information visualization, has famously railed against the “cognitive style” of PowerPoint, which he characterizes as having a “foreshortening of evidence and thought” and a “deeply hierarchical single-⁠path structure.”

    PowerPoint is so ingrained in modern life that the notion of it having a history at all may seem odd. But it does have a very definite lifetime as a commercial product that came onto the scene 30 years ago, in 1987. Remarkably, the founders of the Silicon Valley firm that created PowerPoint did not set out to make presentation software, let alone build a tool that would transform group communication throughout the world. Rather, PowerPoint was a recovery from dashed hopes that pulled a struggling startup back from the brink of failure—and succeeded beyond anything its creators could have imagined.

    PowerPoint was not the first software for creating presentations on personal computers. Starting in 1982, roughly a half-dozen other programs [PDF] came on the market before PowerPoint’s 1987 debut. Its eventual domination was not the result of first-mover advantage. What’s more, some of its most familiar features—the central motif of a slide containing text and graphics; bulleted lists; the slideshow; the slide sorter; and even the animated transitions between slides—did not originate with PowerPoint. And yet it’s become the Kleenex or Scotch Tape of presentation software, as a “PowerPoint” has come to mean any presentation created with software.

    With PowerPoint as well as its predecessors, the motif of the slide was, of course, lifted directly from the world of photography. Some presentation programs actually generated 35-mm slides for display with a slide projector. In most cases, though, the early programs created slides that were printed on paper for incorporation into reports, transferred to transparencies for use on overhead projectors, or saved as digital files to be displayed on computer monitors.

    The upshot was that personal computer users of the 1980s, especially business users, had many options, and the market for business software was undergoing hypergrowth, with programs for generating spreadsheets, documents, databases, and business graphics each constituting a multimillion-dollar category. At the time, commentators saw the proliferation of business software as a new phase in office automation, in which computer use was spreading beyond the accounting department and the typing pool to the office elites. Both the imagined and actual users of the new business software were white-collar workers, from midlevel managers to Mahogany Row executives.

    PowerPoint thus emerged during a period in which personal computing was taking over the American office. A major accelerant was the IBM Personal Computer, which Big Blue unveiled in 1981. By then, bureaucratic America—corporate and government alike—was well habituated to buying its computers from IBM. This new breed of machine, soon known simply as the PC, spread through offices like wildfire.

    The groundwork for that invasion had been laid the previous decade, in the 1970s technosocial vision of the “office of the future.” It started, like so much of what we now take for granted in our contemporary world of networked personal computing, at Xerox’s legendary Palo Alto Research Center (PARC) [PDF]. The site was established in 1970 to invent the computing systems that would equip the future’s white-collar office, an arena the company hoped to dominate in the same way it did photocopying. Many of the bright young computer scientists and engineers recruited to work at PARC knew one another from the major computer science programs funded by the Department of Defense’s Advanced Research Projects Agency (ARPA) at MIT, Carnegie Mellon, Stanford, UC Berkeley, the University of Utah, and SRI.

    In 1972, PARC researchers began to focus on a new personal computer they called the Alto. Led by Alan Kay, Butler Lampson, Bob Taylor, and Chuck Thacker, they were captivated by an extraordinary idea: that in the office of the future, every individual would have a dedicated computer like the Alto. Moreover, these computers would be networked to one another and to other, larger computers, both locally and far away. This networking would form a web of communication and computing resources well beyond the capacity of any single personal computer. In the pursuit of this vision, Ethernet emerged, as did the PARC Universal Packet protocol [PDF], or PUP, an important predecessor of the TCP/IP standard of today’s Internet.

    The Alto’s creators emphasized the machine’s graphics capabilities, dedicating much of the computer’s hardware and software to rendering high-⁠resolution imagery onscreen, including typography, drawings, digital photographs, and animations. It was a huge step up from the mainstream computers of the day, which still used punch cards, paper printouts, teletypes, and “dumb” terminals. Alto users interacted with it through a graphical interface to access, generate, and manipulate information. Even the text was treated as an image. The computer was controlled through a standard keyboard and the then-novel mouse that had emerged from Doug Engelbart’s SRI laboratory.

    This graphical turn in computing was perhaps most pronounced in one of the Alto’s programming languages, called Smalltalk. Developed by Kay, Dan Ingalls, Adele Goldberg, and other collaborators, Smalltalk wasn’t just a programming language; it was also a programming and user environment. It introduced the graphical user interface, or GUI, to personal computing, including a metaphorical desktop with overlapping windows, contextual and pop-up menus, file browsers, scroll bars, selection by mouse clicks, and even cut, copy, and paste.

    img Rob Campbell img Taylor Pohlman After Apple: In 1982, Rob Campbell and Taylor Pohlman quit Apple to found Forethought, with the ambitious goal of creating a graphical-software environment for the IBM PC similar to one developed at Xerox PARC. When their initial plan failed, they pivoted to other projects, including presentation software called Presenter. Photos: Top: Dennis Austin; Bottom: Taylor Pohlman

    While such innovations were ostensibly proprietary, by the end of the 1970s, Xerox managers and PARC staff were routinely discussing their findings with outsiders and publishing details of the Alto system in journals. PARC researchers were, after all, still part of the broader ARPA community of computer scientists and engineers. Many visitors who saw the Alto system considered it transformative.

    One such visitor was Apple cofounder Steve Jobs. Following Xerox’s investment in Apple in 1979, PARC researchers gave Apple engineers and management detailed demonstrations of Smalltalk and other programs previously reserved for Xerox insiders. Jobs was so enthralled by what he saw that he decided to reorient the Lisa, a business computer Apple was developing at the time, to fully embrace the PARC idiom. A few years later, when Jobs was transferred out of the Lisa project, he seized control of another effort aimed at creating a low-cost computer and pushed it, too, toward the PARC idiom. That computer became the Macintosh.

    What does all this have to do with PowerPoint? Apple lavished resources—people and cash alike—to embrace the PARC paradigm with the Lisa and the Macintosh, but not everyone at Apple was happy about that. In particular, those working to maintain the existing Apple II and III lines felt that their efforts were being shortchanged. By 1982, the product marketing manager for the Apple III, Taylor Pohlman, and the software marketing manager for the Apple II and III, Rob Campbell, had had enough. They quit and went into business together, founding the company that would create PowerPoint.

    But PowerPoint was not at all in their original plan.

    One thing that united Pohlman and Campbell—but alienated them at Apple—was that they were cut from a different cloth than the computer-science types working on the Lisa and the Macintosh. Though both Pohlman and Campbell were technically minded, they were also oriented toward marketing and sales. Before Apple, Pohlman had worked in marketing at Hewlett-Packard, and Campbell had run a small accounting software company.

    The pair left Apple late in 1982, and by early 1983, they had secured US $600,000 in venture capital to create a software company, which they called Forethought. Ironically, the startup’s aim was to bring the PARC idiom to the IBM PC and its clones—in essence, to outplay Apple at its own game. That year, the Apple Lisa appeared, priced at nearly $10,000 (more than $25,000 in today’s dollars). Two years earlier, Xerox had brought its own personal computer, the Xerox Star, to market, at an even higher price. Pohlman and Campbell’s idea was to bring a graphical-software environment like the Xerox Alto’s to the hugely popular but graphically challenged PC.

    Forethought’s founders intended to go beyond the Star and the Lisa by incorporating an important dimension of Alto’s Smalltalk: object-oriented programming. In simple terms, traditional programming of the day treated data and the procedures for manipulating it separately. In object-oriented programming, data and procedures are combined in “objects” that interact with each other by passing messages between them. Proponents held that the modularity of object-oriented programming made for speedier development, flexibility, and dynamic change. For example, skilled Smalltalk programmers could quickly alter the GUI while the program was running. Object-oriented programming has since become the prevailing paradigm for the most widely used programming languages.

    Pohlman and Campbell envisioned an object-oriented software platform called Foundation, which was centered around documents. Each Foundation document would act like an object in Smalltalk, which a business user would stitch together with other documents to create, say, a report containing a graph of recent sales, a statistical analysis of customer traits, drawings of proposed changes to a product, and a block of explanatory text. Each element would be live, malleable, and programmable. Spreadsheets, databases, drawings, word processing—Foundation would handle it all. Users would select a document with a mouse click, and contextual menus would then offer choices appropriate for that type of document. Foundation would be, in essence, Smalltalk for the office worker.

    Forethought staffed up, bringing in software developers from Xerox PARC who were familiar with object-oriented programming and WYSIWYG applications, in which the text and graphics displayed on screen look very similar to the way they will appear in print. To create certain functions, the startup inked deals with outside suppliers; Forethought also purchased a powerful VAX computer from Digital Equipment Corp. for the software-development effort.

    Within a year, the company ran into difficulties. For one, the developers grew deeply concerned about which personal computers, if any, would be powerful enough to run Foundation. The Apple Lisa had the horsepower, but it was already failing in the market, while the Macintosh was deemed too feeble. And the IBM PC was still far behind where Forethought had hoped and planned it would be.

    Forethought, a Silicon Valley startup, brought PowerPoint 1.0 to market in April 1987.1/3

    Forethought, a Silicon Valley startup, brought PowerPoint 1.0 to market in April 1987. Image: Dennis Austin

    Version 1.0 included a humorous sample presentation in which Christopher Columbus pitches Queen Isabella on his expedition.2/3

    Version 1.0 included a humorous sample presentation in which Christopher Columbus pitches Queen Isabella on his expedition. Image: Dennis Austin

    Many elements of today’s PowerPoint were present in version 1.0, including its graphical, WYSIWYG environment; the slide sorter and slideshow; and the ability to easily format and size text, combine text with images, and draw simple lines and shapes.3/3

    Many elements of today’s PowerPoint were present in version 1.0, including its graphical, WYSIWYG environment; the slide sorter and slideshow; and the ability to easily format and size text, combine text with images, and draw simple lines and shapes. Image: Dennis Austin

    More worrying was Oracle’s announcement that it would need another year to deliver on its contract for the database code. This meant that the launch of Foundation would be intolerably delayed. Forethought was running perilously low on funds, and it didn’t have the resources to develop a database on its own. The company was facing, literally, an existential crisis.

    Rather than liquidate the firm, management and investors decided to “restart” Forethought—a “pivot” in today’s Silicon Valley parlance. Work on Foundation was set aside, while the firm focused on software publishing—that is, manufacturing, marketing, and supporting computer programs written by others. Forethought’s publishing arm produced software for the Apple Macintosh under the brand Macware. And it was a success. Its biggest hit, oddly enough, was a database program called FileMaker.

    Photo of Robert Gaskins Robert Gaskins Photo of Dennis Austin Dennis Austin Photo of Tom Rudkin Tom Rudkin Programming PowerPoint: Robert Gaskins was in charge of Forethought’s product development and, with Dennis Austin and Tom Rudkin, developed Presenter, which eventually was renamed PowerPoint. Several months after the release of PowerPoint 1.0 in 1987, Microsoft acquired Forethought. Rudkin and Austin continued as PowerPoint’s principal developers, and Gaskins led Microsoft’s Graphics Business Unit. Photos: Dennis Austin

    With brightening finances from sales of FileMaker, Forethought began to develop a new software product of its own. This new effort was the brainchild of Robert Gaskins, an accomplished computer scientist who’d been hired to lead Forethought’s product development. Gaskins was a polymath who had simultaneously pursued Ph.D.s in English, linguistics, and computer science at UC Berkeley before joining industry. He in turn hired a bright young software developer named Dennis Austin, who had previously developed compilers at Burroughs and contributed to a GUI operating system at a laptop startup.

    Gaskins and Austin worked closely to conceptualize, design, and specify Forethought’s new product. Gaskins spotted an opportunity in presentation software and believed they could apply the PARC idiom to this application. He envisioned the user creating slides of text and graphics in a graphical, WYSIWYG environment, then outputting them to 35-mm slides, overhead transparencies, or video displays and projectors, and also sharing them electronically through networks and electronic mail. The presentation would spring directly from the mind of the business user, without having to first transit through the corporate art department.

    While Gaskins’s ultimate aim for this new product, called Presenter, was to get it onto IBM PCs and their clones, he and Austin soon realized that the Apple Macintosh was the more promising initial target. Designs for the first version of Presenter specified a program that would allow the user to print out slides on Apple’s newly released laser printer, the LaserWriter, and photocopy the printouts onto transparencies for use with an overhead projector.

    Austin quickly got to work programming Presenter in Apple Pascal on a Lisa computer, eventually switching to a Macintosh. He was joined in the effort by Tom Rudkin, an experienced developer, and the pair hewed as closely as possible to the Macintosh’s user interface and modes of operation. Indeed, the source code for Presenter included Apple-provided code for handling text, which Apple used in its own word processor, MacWrite.

    In April 1987, Forethought introduced its new presentation program to the market very much as it had been conceived, but with a different name. Presenter was now PowerPoint 1.0—there are conflicting accounts of the name change—and it was a proverbial overnight success with Macintosh users. In the first month, Forethought booked $1 million in sales of PowerPoint, at a net profit of $400,000, which was about what the company had spent developing it. And just over three months after PowerPoint’s introduction, Microsoft purchased Forethought outright for $14 million in cash.

    PowerPoint then became Microsoft’s presentation software, first just for the Macintosh and later also for Windows. The Forethought team became Microsoft’s Graphics Business Unit, which Gaskins led for five years, while Austin and Rudkin remained the principal developers of PowerPoint for about 10 years. Wisely, Microsoft chose to keep the Graphics Business Unit in Silicon Valley rather than move it to Redmond, Wash. The unit became Microsoft’s first outpost in the region, and PowerPoint is still developed there to this day.

    While PowerPoint was a success from the start, it nevertheless faced stiff competition, and for several years, Lotus Freelance and Software Publishing’s Harvard Graphics commanded larger market shares. The tipping point for PowerPoint came in 1990, when Microsoft unleashed its bundling strategy and began selling Microsoft Office—which combined Microsoft Word, Excel, and PowerPoint—as a $1,000 set. Previously, each part had been sold separately for about $500 apiece.

    Because most users of personal computers required both a word processor and a spreadsheet program, Microsoft’s price for Office proved compelling. PowerPoint’s competitors, on the other hand, resented the tactic as giving away PowerPoint for free. And for more than a quarter century, Microsoft’s competitive logic proved unassailable.

    These days, the business software market is shifting again, and Microsoft Office must now compete with similar bundles that are entirely free, from the likes of Google, LibreOffice, and others. Productivity software resides more often than not in the cloud, rather than on the user’s device. Meanwhile, the dominant mode of personal computing globally has firmly shifted from the desktop and laptop to the smartphone. As yet, no new vision of personal computing like the one that came from Xerox PARC in the 1970s has emerged. And so for the moment, it appears that PowerPoint, as we know it, is here to stay.

    David C. Brock is a historian of technology and director of the Center for Software History at the Computer History Museum, in Mountain View, Calif.



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