Guide Software Verification and Validation for Practitioners and Managers, Second Edition

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Prime Book Box for Kids. Customers who viewed this item also viewed. Page 1 of 1 Start over Page 1 of 1. Software Verification and Validation: About the Author Rakitin has nearly 25 years of experience as a software engineer and a software quality professional. Artech House July 31, Language: Related Video Shorts 0 Upload your video.

Faculty of Electrical Engineering and Computer Science

Share your thoughts with other customers. Write a customer review. There was a problem filtering reviews right now. Please try again later. This book is more like a joke, a classic example of cut and paste authors writing for a highly specialized topic with no technical weight. It never should be used for computer science students unless the instructor himself has no clue about the subject.

This book is for people with no formal training who want to learn some random buzzwords. One person found this helpful. I wish I could count the times I've seen second and third rounds of development occur because the first round produced a working version of a product the customer didn't want. With all of the emphasis lately on rapid development, especially now that the web has everyone working on "internet time", there has been a noticeable lack of discussion on ensuring the software produced fits the needs of the customer and is of reasonable quality.

Rakitin addresses these issues and more in this book. As the title indicates, he concentrates on Verification "are we building the product right? However, the subtitle to the work "A Practitioner's Guide" provides much more insight into the actual scope of this work. In the discussion of software inspection meetings, for example, Rakitin give guidelines regarding not only the mechanics of who should attend and when materials should be distributed but he also provides insight into what to expect as a moderator and how much should be expected to be accomplished in the meetings themselves.

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There are a number of statements in the book that begin "Experience has shown This isn't to say there couldn't be more. Although what's presented is very good, there are points in the book where I found myself wishing for additional discussion. Perhaps in future editions Rakitin will be able to expand upon, say, requirements collection or configuration management. There are also things that could be updated if the book were to have a revision. This could obviously be expanded to cover the Rational Unified Process, Rational's effort to provide UML with "meat" the modeling language alone could not have.

As Deming observed and Rakitin noted, "The quality of a product is directly related to the quality of the process used to create it. These items, which appear in approximately 80 pages of appendices, give the reader a variety of starting places for just such an initiative.

Brooks said "no silver bullet" and he was right. Quality software is possible only through a methodical, rational, and scientific approach. Rakitin goes a long way towards that in this work. I highly recommend it. Software Verification and Validation by Steven R. A practical book by a practical author, presenting the steps required to set up a formal and complete Verification and Validation program for software production. Rakitin sprinkles pertinent quotations throughout the book to support his case that software quality programs detection cost less than simply releasing a "buggy" program to your Customers.

At these lectures, the author fleshes out and makes more interesting many of the points made in his book. Depending on the organization's expectations for software development, Development Testing might include static code analysis , data flow analysis, metrics analysis, peer code reviews, unit testing, code coverage analysis, traceability, and other software testing practices. Customers are routed to either a current version control of a feature, or to a modified version treatment and data is collected to determine which version is better at achieving the desired outcome.

In concurrent testing, the focus is on the performance while continuously running with normal input and under normal operational conditions, as opposed to stress testing, or fuzz testing. Memory leak, as well as basic faults are easier to find with this method. In software testing, conformance testing verifies that a product performs according to its specified standards.

Compilers, for instance, are extensively tested to determine whether they meet the recognized standard for that language. A common practice in waterfall development is that testing is performed by an independent group of testers. However, even in the waterfall development model, unit testing is often done by the software development team even when further testing is done by a separate team.

In contrast, some emerging software disciplines such as extreme programming and the agile software development movement, adhere to a " test-driven software development " model. In this process, unit tests are written first, by the software engineers often with pair programming in the extreme programming methodology. The tests are expected to fail initially. Each failing test is followed by writing just enough code to make it pass.

This means the test suites are continuously updated as new failure conditions and corner cases are discovered, and they are integrated with any regression tests that are developed. Unit tests are maintained along with the rest of the software source code and generally integrated into the build process with inherently interactive tests being relegated to a partially manual build acceptance process.

The ultimate goals of this test process are to support continuous integration and to reduce defect rates. This methodology increases the testing effort done by development, before reaching any formal testing team. In some other development models, most of the test execution occurs after the requirements have been defined and the coding process has been completed.

Although variations exist between organizations, there is a typical cycle for testing. The same practices are commonly found in other development models, but might not be as clear or explicit. Many programming groups [ Like whom? There are many frameworks [ specify ] to write tests in, and continuous integration software will run tests automatically every time code is checked into a version control system. While automation cannot reproduce everything that a human can do and all the ways they think of doing it , it can be very useful for regression testing.

However, it does require a well-developed test suite of testing scripts in order to be truly useful. Program testing and fault detection can be aided significantly by testing tools and debuggers. There are a number of frequently used software metrics , or measures, which are used to assist in determining the state of the software or the adequacy of the testing. Based on the amount of test cases required to construct a complete test suite in each context i.

It has been proved that each class is strictly included in the next. For instance, testing when we assume that the behavior of the implementation under test can be denoted by a deterministic finite-state machine for some known finite sets of inputs and outputs and with some known number of states belongs to Class I and all subsequent classes. However, if the number of states is not known, then it only belongs to all classes from Class II on.

If the implementation under test must be a deterministic finite-state machine failing the specification for a single trace and its continuations , and its number of states is unknown, then it only belongs to classes from Class III on. Testing temporal machines where transitions are triggered if inputs are produced within some real-bounded interval only belongs to classes from Class IV on, whereas testing many non-deterministic systems only belongs to Class V but not all, and some even belong to Class I. The inclusion into Class I does not require the simplicity of the assumed computation model, as some testing cases involving implementations written in any programming language, and testing implementations defined as machines depending on continuous magnitudes, have been proved to be in Class I.

Other elaborated cases, such as the testing framework by Matthew Hennessy under must semantics, and temporal machines with rational timeouts, belong to Class II. A software testing process can produce several artifacts. The actual artifacts produced are a factor of the software development model used, stakeholder and organisational needs. Several certification programs exist to support the professional aspirations of software testers and quality assurance specialists.


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Note that a few practitioners argue that the testing field is not ready for certification, as mentioned in the Controversy section. Some of the major software testing controversies include:. It is commonly believed that the earlier a defect is found, the cheaper it is to fix it. The following table shows the cost of fixing the defect depending on the stage it was found.

With the advent of modern continuous deployment practices and cloud-based services, the cost of re-deployment and maintenance may lessen over time. The "smaller projects" curve turns out to be from only two teams of first-year students, a sample size so small that extrapolating to "smaller projects in general" is totally indefensible. The GTE study does not explain its data, other than to say it came from two projects, one large and one small.

The paper cited for the Bell Labs "Safeguard" project specifically disclaims having collected the fine-grained data that Boehm's data points suggest. The IBM study Fagan's paper contains claims that seem to contradict Boehm's graph and no numerical results that clearly correspond to his data points.

Boehm doesn't even cite a paper for the TRW data, except when writing for "Making Software" in , and there he cited the original article. There exists a large study conducted at TRW at the right time for Boehm to cite it, but that paper doesn't contain the sort of data that would support Boehm's claims.

Software testing is used in association with verification and validation: The terms verification and validation are commonly used interchangeably in the industry; it is also common to see these two terms defined with contradictory definitions. The contradiction is caused by the use of the concepts of requirements and specified requirements but with different meanings. In the case of IEEE standards, the specified requirements, mentioned in the definition of validation, are the set of problems, needs and wants of the stakeholders that the software must solve and satisfy.

And, the products mentioned in the definition of verification, are the output artifacts of every phase of the software development process. These products are, in fact, specifications such as Architectural Design Specification, Detailed Design Specification, etc. The SRS is also a specification, but it cannot be verified at least not in the sense used here, more on this subject below.

But, for the ISO , the specified requirements are the set of specifications, as just mentioned above, that must be verified. A specification, as previously explained, is the product of a software development process phase that receives another specification as input. A specification is verified successfully when it correctly implements its input specification. All the specifications can be verified except the SRS because it is the first one it can be validated, though.

Both the SRS and the software must be validated. The SRS can be validated statically by consulting with the stakeholders. Nevertheless, running some partial implementation of the software or a prototype of any kind dynamic testing and obtaining positive feedback from them, can further increase the certainty that the SRS is correctly formulated. On the other hand, the software, as a final and running product not its artifacts and documents, including the source code must be validated dynamically with the stakeholders by executing the software and having them to try it.

Thinking this way is not advisable as it only causes more confusion. It is better to think of verification as a process involving a formal and technical input document. Software testing may be considered a part of a software quality assurance SQA process. They examine and change the software engineering process itself to reduce the number of faults that end up in the delivered software: What constitutes an acceptable defect rate depends on the nature of the software; A flight simulator video game would have much higher defect tolerance than software for an actual airplane.

Although there are close links with SQA, testing departments often exist independently, and there may be no SQA function in some companies. Software testing is an activity to investigate software under test in order to provide quality-related information to stakeholders. By contrast, QA quality assurance is the implementation of policies and procedures intended to prevent defects from reaching customers. From Wikipedia, the free encyclopedia.

Software Verification and Validation for Practitioners and Managers, Second Edition

Graphical user interface testing. Exception handling and Recovery testing. Capability Maturity Model Integration and Waterfall model. Verification and validation software and Software quality control.

Software Verification and Validation for Practitioners and Managers

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Verification and Validation

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The Leprechauns of Software Engineering: How folklore turns into fact and what to do about it. Retrieved August 13, Major fields of computer science. Computer architecture Embedded system Real-time computing Dependability. Network architecture Network protocol Network components Network scheduler Network performance evaluation Network service. Interpreter Middleware Virtual machine Operating system Software quality. Programming paradigm Programming language Compiler Domain-specific language Modeling language Software framework Integrated development environment Software configuration management Software library Software repository.

Software development process Requirements analysis Software design Software construction Software deployment Software maintenance Programming team Open-source model. Model of computation Formal language Automata theory Computational complexity theory Logic Semantics. Algorithm design Analysis of algorithms Algorithmic efficiency Randomized algorithm Computational geometry. Discrete mathematics Probability Statistics Mathematical software Information theory Mathematical analysis Numerical analysis. Database management system Information storage systems Enterprise information system Social information systems Geographic information system Decision support system Process control system Multimedia information system Data mining Digital library Computing platform Digital marketing World Wide Web Information retrieval.

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