.NET bar code Coverage Criteria in Software Create pdf417 2d barcode in Software Coverage Criteria

Coverage Criteria use none none printer toadd none in nonegenerator barcode using with each other operato none none r and the entire expression with true, false, leftop and rightop. leftop and rightop are special mutation operators that return the left side and the right side, respectively, of a relational expression. The mutation operator that removes each statement in the program is called Statement DeLetion (SDL) in Mothra [187].

A number of authors [14, 15, 37, 298, 350] have used traces from model checkers to generate tests, including mutation based tests. The text from Huth and Ryan [173] provides a easily accessible introduction to model checking and discusses use of the SMV system. One of the key technologies being used to transmit data among heterogeneous software components on the Web is the eXtensible Markup Language (XML) [1, 43].

Data-based mutation de nes generic classes of mutation operators. These mutation operator classes are intended to work with different grammars. The current literature [203] cites operator classes that modify the length of value strings and determine whether or not a value is in a prede ned set of values.

. Microsoft Office Excel Website NOTES 1 There is no relations none none hip between this use of mutation and genetic algorithms, except that both make an analogy to biological mutation. Mutation for testing predated genetic algorithms by a number of years. 2 Of course, since mutant detection is undecidable, a heuristic is the best option possible.

3 This goal is in some sense equivalent to the goal of forcing each clause in each predicate to make a difference. 4 Jester s web page is http://jester.sourceforge.

net/. PART 3 Applying Criteria in Practice Practical Considerations The rst ve chapters o none none f this book help practical testers ll up their toolbox with various test criteria. While studying test criteria in isolation is needed, one of the most dif cult obstacles to a software organization moving to level 3 or 4 testing is integrating effective testing strategies into the overall software development process. This chapter discusses various issues involved with applying test criteria during software development.

The overriding philosophy is to think: if the tester looks at this as a technical problem, and seeks technical solutions, he or she will usually nd that the obstacles are not as large as they rst seem.. 6.1 REGRESSION TESTING Regression testing is t none none he process of re-testing software that has been modi ed. Regression testing constitutes the vast majority of testing effort in commercial software development and is an essential part of any viable software development process. Large components or systems tend to have large regression test suites.

Even though many developers don t want to believe it (even when faced with indisputable evidence!), small changes to one part of a system often cause problems in distant parts of the system. Regression testing is used to nd this kind of problem. It is worth emphasizing that regression tests must be automated.

Indeed, it could be said that unautomated regression testing is equivalent to no regression testing. A wide variety of commercially available tools are available. Capture/replay tools automate testing of programs that use graphical user interfaces.

Version control software, already in use to manage different versions of a given system, effectively manages the test sets associated with each version. Scripting software manages the process of obtaining test inputs, executing the software, marshaling the outputs, comparing the actual and expected outputs, and generating test reports. The aim of this section is to explain what kinds of tests ought to be in a regression test set, which regression tests to run, and how to respond to regression tests that fail.

We treat each of these issues in turn. We direct the reader interested in detail on any of these topics to the bibliographic notes..

Applying Criteria in Practice The test engineer faces none for none a Goldilocks problem in determining which tests to include in a regression test set. Including every test set possible results in an unmanageably large regression test set. The result is that the test set cannot be run as often as changes are made to the software.

For many development organizations, this period amounts to a day; regression tests run at night to evaluate software changed that day, with developers reviewing the results the following morning. If the regression tests do not nish in a timely manner, the development process is disrupted. It is well worth throwing money at this problem in terms of additional computational resources to execute the tests, but, at some point, the marginal advantage of adding a given test is not worth the marginal expenditure of the resources needed to execute it.

On the other side, a set that is too small will not cover the functionality of the software suf ciently well, and too many faults will make it past the regression test set to the customer. The prior paragraph does not actually say which tests are in the regression test set, just that the set has to be the right size. Some organizations have a policy that for each problem report that has come in from the eld, a regression test exists that, in principle, detects the problem.

The idea is that customers are more willing to be saddled with new problems than with the same problem over and over. The above approach has a certain charm from the perspective of traceability, in that each test chosen in this way has a concrete rationale. The coverage criteria that form the heart of this book provide an excellent basis for evaluating regression test sets.

For example, if node coverage in the form of method call coverage shows that some methods are never invoked, then it is a good idea to either decide that the method is dead code with respect that particular application, or include a test that results in a call to the method. If one or more regression tests fail, the rst step is to determine if the change to the software is faulty, or if the regression test set itself is broken. In either case, additional work is required.

If no regression tests fail, there is still work to do. The reason is that a regression test set that is satisfactory for a given version of the software is not necessarily satisfactory for a subsequent version. Changes to software are often classi ed as corrective, perfective, adaptive, and preventive.

All of these changes require regression testing. Even when the (desired) external functionality of the software does not change, the regression test set still needs to be reanalyzed to see if it is adequate. For example, preventive maintenance may result in wholesale internal restructuring of some components.

If the criteria used to select the original regression tests were derived from the structure of the implementation, then it is unlikely that the test set will adequately cover the new implementation. Evolving a regression test set as the associated software changes is a challenge. Changes to the external interface are particularly painful, since such a change can cause all tests to fail.

For example, suppose that a particular input moves from one drop-down menu to another. The result is that the capture/playback aspect of executing each test case needs an update. Or suppose that the new version of the software generates an additional output.

All of the expected results are now out of date, and need to be augmented. Clearly, automated support for maintaining test sets is just as crucial as automated support for executing the tests. Adding a (small) number of tests to a regression test set is usually simple.

The marginal cost of each additional test is typically quite small. Cumulatively, however,.
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