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Some testing may thus be performed in the development process, SQA process, or V&V process, again depending on project organization. Because SQM plans address testing, this section includes some comments on testing.

Testing

The assurance processes described in SQA and V&V examine every output relative to the software requirement specification to ensure the output’s traceability, consistency, completeness, correctness, and performance. This confirmation also includes the outputs of the development and maintenance processes, collecting, analyzing, and measuring the results. SQA ensures that appropriate types of tests are planned, developed, and implemented, and V&V develops test plans, strategies, cases, and procedures.

Two types of testing may fall under the headings SQA and V&V, because of their responsibility for the quality of the materials used in the project:

  • Evaluation and test of tools to be used on the project (IEEE1462-98)
  • Conformance test (or review of conformance test) of components and COTS products to be used in the product; there now exists a standard for software packages (IEEE1465-98)

Sometimes an independent V&V organization may be asked to monitor the test process and sometimes to witness the actual execution to ensure that it is conducted in accordance with specified procedures. Again, V&V may be called upon to evaluate the testing itself: adequacy of plans and procedures, and adequacy and accuracy of results.

Another type of testing that may fall under the heading of V&V organization is third-party testing. The third party is not the developer, nor is in any way associated with the development of the product. Instead, the third party is an independent facility, usually accredited by some body of authority. Their purpose is to test a product for conformance to a specific set of requirements.

Software quality measurement

The models of software product quality often include measures to determine the degree of each quality characteristic attained by the product.

If they are selected properly, measures can support software quality (among other aspects of the software life cycle processes) in multiple ways. They can help in the management decision-making process. They can find problematic areas and bottlenecks in the software process; and they can help the software engineers assess the quality of their work for SQA purposes and for longer-term process quality improvement.

With the increasing sophistication of software, questions of quality go beyond whether or not the software works to how well it achieves measurable quality goals.

There are a few more topics where measurement supports SQM directly. These include assistance in deciding when to stop testing. For this, reliability models and benchmarks, both using fault and failure data, are useful.

The cost of SQM processes is an issue which is almost always raised in deciding how a project should be organized. Often, generic models of cost are used, which are based on when a defect is found and how much effort it takes to fix the defect relative to finding the defect earlier in the development process. Project data may give a better picture of cost.

Finally, the SQM reports themselves provide valuable information not only on these processes, but also on how all the software life cycle processes can be improved.

While the measures for quality characteristics and product features may be useful in themselves (for example, the number of defective requirements or the proportion of defective requirements), mathematical and graphical techniques can be applied to aid in the interpretation of the measures. These fit into the following categories:

  • Statistically based (for example, Pareto analysis, runcharts, scatter plots, normal distribution)
  • Statistical tests (for example, the binomial test, chi-squared test)
  • Trend analysis
  • Prediction (for example, reliability models)

The statistically based techniques and tests often provide a snapshot of the more troublesome areas of the softwareproduct under examination. The resulting charts andgraphs are visualization aids which the decision-makerscan use to focus resources where they appear most needed. Results from trend analysis may indicate that a schedule has not been respected, such as in testing, or that certain classes of faults will become more intense unless some corrective action is taken in development. The predictive techniques assist in planning test time and in predicting failure.

References:

http://en.wikipedia.org/wiki/Software_quality_assurance, http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-171Fall2003/CourseHome/,http://www.cs.cornell.edu/courses/cs501/2008sp/, http://www.comp.lancs.ac.uk/computing/resources/IanS/SE7/,http://www.ee.unb.ca/kengleha/courses/CMPE3213/IntroToSoftwareEng.htm, http://www.softwarecertifications.org/qai_cmsq.htm,http://satc.gsfc.nasa.gov/assure/agbsec3.txt, etc...

Questions & Answers

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Mark Reply
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own Reply
team a found 342 errors during the software engineering process prior to release. team b found 184 errors. what additional measures would have to be made for projects a and b to determine which of the teams eliminated errors more efficiently? what metrics would you propose to help in making the determination? what historical data might be useful?
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Source:  OpenStax, Software engineering. OpenStax CNX. Jul 29, 2009 Download for free at http://cnx.org/content/col10790/1.1
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