Guided Mutation Testing for JavaScript Web Applications

Mutation testing is an effective test adequacy assessment technique. However, there is a high computational cost in executing the test suite against a potentially large pool of generated mutants. Moreover, there is much effort involved in filtering out equivalent mutants. Prior work has mainly focus...

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Vydáno v:IEEE transactions on software engineering Ročník 41; číslo 5; s. 429 - 444
Hlavní autoři: Mirshokraie, Shabnam, Mesbah, Ali, Pattabiraman, Karthik
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2015
IEEE Computer Society
Témata:
Algorithms
Complexity theory
equivalent mutants
guided mutation generation
Heuristic algorithms
IEEE Computer Society
Instruments
Java
JavaScript
Measurement
Mutation
mutation testing
Performance analysis
Software engineering
Studies
Testing
web applications
web applications
Mutation testing
equivalent mutants
guided mutation generation
JavaScript
ISSN:0098-5589, 1939-3520
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Shrnutí:Mutation testing is an effective test adequacy assessment technique. However, there is a high computational cost in executing the test suite against a potentially large pool of generated mutants. Moreover, there is much effort involved in filtering out equivalent mutants. Prior work has mainly focused on detecting equivalent mutants after the mutation generation phase, which is computationally expensive and has limited efficiency. We propose an algorithm to select variables and branches for mutation as well as a metric, called FunctionRank, to rank functions according to their relative importance from the application's behaviour point of view. We present a technique that leverages static and dynamic analysis to guide the mutation generation process towards parts of the code that are more likely to influence the program's output. Further, we focus on the JavaScript language, and propose a set of mutation operators that are specific to Web applications. We implement our approach in a tool called MUTANDIS. The results of our empirical evaluation show that (1) more than 93 percent of generated mutants are non-equivalent, and (2) more than 75 percent of the surviving non-equivalent mutants are in the top 30 percent of the ranked functions.
Bibliografie:SourceType-Scholarly Journals-1
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2014.2371458