Introduction to Software Testing

Ammann, Paul; Offutt, Jeff

doi:10.1017/cbo9780511809163

Cited by 659 publications

(282 citation statements)

References 0 publications

Supporting

Mentioning

267

Contrasting

Unclassified

Order By: Relevance

“…Mutation analysis [3] is the process of systematically seeding syntactic changes into a program to determine whether the test cases can detect the resulting semantic program mutants. Undetected ("live") mutants can guide the tester in improving a test suite, while detected ("killed") mutants are used to quantify the effectiveness of a test suite in terms of its mutation score that is calculated as the ratio of killed mutants to all mutants.…”

Section: Mutation Analysismentioning

confidence: 99%

“…With appropriate mutation operators, mutation analysis subsumes several traditional coverage criteria such as branch coverage [3]. We are not aware of any study on relationship of mutation analysis and predicate coverage.…”

Section: Mutation Analysismentioning

confidence: 99%

“…The comparison metrics were basic block coverage and a simplified version of predicate coverage [11] that measures how many combinations of program predicates are covered (which differs from the traditional condition or MCDC coverages [3]). The results showed that among the advanced techniques the best was shape abstraction (described in detail in Section 4.2).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

Abstract. Container classes such as lists, sets, or maps are elementary data structures common to many programming languages. Since they are a part of standard libraries, they are important to test, which led to research on advanced testing techniques targeting such containers and research on comparing testing techniques using such containers. However, these techniques have not been thoroughly compared to simpler techniques such as random testing. We present the results of a larger case study in which we compare random testing with shape abstraction, a systematic technique that showed the best results in a previous study. Our experiments show that random testing is about as effective as shape abstraction for testing these containers, which raises the question whether containers are well suited as a benchmark for comparing advanced testing techniques.

show abstract

Section: Mutation Analysismentioning

confidence: 99%

Section: Mutation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

show abstract

“…In [71], this drawback is exemplified using the regular grammar that is drawn from an example used by Ammann and Offutt [72]. Belli and Beyazit [71] have slightly, nevertheless equivalently, reformatted the example for saving space, where the nonterminal duplication mutant is a CFG.…”

Section: Model-based Mutation Testingmentioning

confidence: 99%

Model-based mutation testing—Approach and case studies

Belli

Budnik

Hollmann³

et al. 2016

Science of Computer Programming

View full text Add to dashboard Cite

This paper rigorously introduces the concept of model-based mutation testing (MBMT) and positions it in the landscape of mutation testing. Two elementary mutation operators, insertion and omission, are exemplarily applied to a hierarchy of graph-based models of increasing expressive power including directed graphs, event sequence graphs, finite-state machines and statecharts. Test cases generated based on the mutated models (mutants) are used to determine not only whether each mutant can be killed but also whether there are any faults in the corresponding system under consideration (SUC) developed based on the original model. Novelties of our approach are: (1) evaluation of the fault detection capability (in terms of revealing faults in the SUC) of test sets generated based on the mutated models, and (2) superseding of the great variety of existing mutation operators by iterations and combinations of the two proposed elementary operators. Three case studies were conducted on industrial and commercial real-life systems to demonstrate the feasibility of using the proposed MBMT approach in detecting faults in SUC, and to analyze its characteristic features. Our experimental data suggest that test sets generated based on the mutated models created by insertion operators are more effective in revealing faults in SUC than those generated by omission operators. Worth noting is that test sets following the MBMT approach were able to detect faults in the systems that were tested by manufacturers and independent testing organizations before they were released.

show abstract

“…According to this result we can enhance the test set by adding a test in which the input is x=4, y=4 and another test in which the input is x=4, y=3 in order to kill these two live mutants respectively and get a higher mutant kill rate (the highest kill rate is 1, as this example shows). Many studies provide evidence that traditional mutation testing is a very rigorous test assessment technique, so it is often used to assess other test techniques [2,14]. However, the mutation operators used to guide mutant generation may lead to a large number of mutants because a single mutation operator has to be applied to each relevant point in the program and a single mutant only contains a modification to a single relevant point (as shown in Figure 1).…”

Section: 1! Traditional Mutation Testingmentioning

confidence: 99%

Semantic Mutation Testing for Multi-agent Systems

Alexander

2015

Engineering Multi-Agent Systems

View full text Add to dashboard Cite

Abstract. This paper introduces semantic mutation testing (SMT) into multiagent systems. SMT is a test assessment technique that makes changes to the interpretation of a program and then examines whether a given test set has the ability to detect each change to the original interpretation. These changes represent possible misunderstandings of how the program is interpreted. SMT can also be used to assess robustness to and reliability of semantic changes. This paper applies SMT to three rule-based agent programming languages, namely Jason, GOAL and 2APL, provides several contexts in which SMT for these languages is useful, and proposes three sets of semantic mutation operators (i.e., rules to make semantic changes) for these languages respectively, and a systematic approach to derivation of semantic mutation operators for rule-based agent languages. This paper then shows, through preliminary evaluation of our semantic mutation operators for Jason, that SMT has some potential to assess tests, robustness to and reliability of semantic changes.

show abstract

Introduction to Software Testing

Cited by 659 publications

References 0 publications

Testing Container Classes: Random or Systematic?

Testing Container Classes: Random or Systematic?

Model-based mutation testing—Approach and case studies

Semantic Mutation Testing for Multi-agent Systems

Contact Info

Product

Resources

About