Mutation Testing is a fault-based software testing technique that has been widely studied for over three decades The literature on Mutation Testing has contributed a set of approaches, tools, developments, and empirical results This paper provides a comprehensive analysis and survey of Mutation Testing The paper also presents the results of several development trend analyses These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation Testing itself is the subject of increasing interest

An Analysis and Survey of the Development of Mutation Testing

Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research. Copyright © 2010 John Wiley & Sons, Ltd.

Regression testing minimization, selection and prioritization: a survey

Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the design of our infrastructure. The paper then describes the infrastructure that we are creating in response to these challenges, and that we are now making available to other researchers, and discusses the impact that this infrastructure has had and can be expected to have.

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Supporting Controlled Experimentation with Testing Techniques: An Infrastructure and its Potential Impact

Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.

A Survey on Software Fault Localization

Conformance testing in model-based development refers to the testing activity that verifies whether the code generated (manually or automatically) from the model is behaviorally equivalent to the model. Presently the adequacy of conformance testing is inferred by measuring structural coverage achieved over the model. We hypothesize that adequacy metrics for conformance testing should consider structural coverage over the requirementseither in place of or in addition to structural coverage over the model. Measuring structural coverage over the requirements gives a notion of how well the conformance tests exercise the required behavior of the system.

We conducted an experiment to investigate the hypothesis stating structural coverage over formal requirements is more effective than structural coverage over the model as an adequacy measure for conformance testing. We found that the hypothesis was rejected at 5% statistical significance on three of the four case examples in our experiment. Nevertheless, we found that the tests providing requirements coverage found several faults that remained undetected by tests providing model coverage. We thus formed a second hypothesis stating that complementing model coverage with requirements coverage will prove more effective as an adequacy measure than solely using model coverage for conformance testing. In our experiment, we found test suites providing both requirements coverage and model coverage to be more effective at finding faults than test suites providing model coverage alone, at 5% statistical significance. Based on our results, we believe existing adequacy measures for conformance testing that only consider model coverage can be strengthened by combining them with rigorous requirements coverage metrics.

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Requirements Coverage as an Adequacy Measure for Conformance Testing

The empirical assessment of test techniques plays an important role in software testing research. One common practice is to instrument faults, either manually or by using mutation operators. The latter allows the systematic, repeatable seeding of large numbers of faults; however, we do not know whether empirical results obtained this way lead to valid, representative conclusions. This paper investigates this important question based on a number of programs with comprehensive pools of test cases and known faults. It is concluded that, based on the data available thus far, the use of mutation operators is yielding trustworthy results (generated mutants are similar to real faults). Mutants appear however to be different from hand-seeded faults that seem to be harder to detect than real faults.

L.C. Briand

Papers

Is mutation an appropriate tool for testing experiments? [software testing]