Search-based fault localisation: A systematic mapping study
Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been successfully applied in the field of Search-Based Fault L...
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| Vydáno v: | Information and software technology Ročník 123; s. 106295 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.07.2020
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| ISSN: | 0950-5849, 1873-6025 |
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| Abstract | Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been successfully applied in the field of Search-Based Fault Localisation (SBFL). However, there is no study mapping the SBFL field to the best of our knowledge and we believe that such a map is important to promote new advances in this field.
To present the results of a mapping study on SBFL, by characterising the proposed methods, identifying sources of used information, adopted evaluation functions, applied algorithms and elements regarding reported experiments.
Our mapping followed a defined process and a search protocol. The conducted analysis considers different dimensions and categories related to the main characteristics of SBFL methods.
All methods are grounded on the coverage spectra category. Overall the methods search for solutions related to suspiciousness formulae to identify possible faulty code elements. Most studies use evolutionary algorithms, mainly Genetic Programming, by using a single-objective function. There is little investigation of real-and-multiple-fault scenarios, and the subjects are mostly written in C and Java. No consensus was observed on how to apply the evaluation metrics.
Search-based fault localisation has seen a rise in interest in the past few years and the number of studies has been growing. We identified some research opportunities such as exploring new sources of fault data, exploring multi-objective algorithms, analysing benchmarks according to some classes of faults, as well as, the use of a unique definition for evaluation measures. |
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| AbstractList | Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been successfully applied in the field of Search-Based Fault Localisation (SBFL). However, there is no study mapping the SBFL field to the best of our knowledge and we believe that such a map is important to promote new advances in this field.
To present the results of a mapping study on SBFL, by characterising the proposed methods, identifying sources of used information, adopted evaluation functions, applied algorithms and elements regarding reported experiments.
Our mapping followed a defined process and a search protocol. The conducted analysis considers different dimensions and categories related to the main characteristics of SBFL methods.
All methods are grounded on the coverage spectra category. Overall the methods search for solutions related to suspiciousness formulae to identify possible faulty code elements. Most studies use evolutionary algorithms, mainly Genetic Programming, by using a single-objective function. There is little investigation of real-and-multiple-fault scenarios, and the subjects are mostly written in C and Java. No consensus was observed on how to apply the evaluation metrics.
Search-based fault localisation has seen a rise in interest in the past few years and the number of studies has been growing. We identified some research opportunities such as exploring new sources of fault data, exploring multi-objective algorithms, analysing benchmarks according to some classes of faults, as well as, the use of a unique definition for evaluation measures. |
| ArticleNumber | 106295 |
| Author | Camilo-Junior, Celso G. Harrison, Rachel Vergilio, Silvia R. Freitas, Diogo M. Leitao-Junior, Plinio S. |
| Author_xml | – sequence: 1 givenname: Plinio S. surname: Leitao-Junior fullname: Leitao-Junior, Plinio S. email: plinio@inf.ufg.br organization: Instituto de Informática, Universidade Federal de Goiás, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, Goiás, 74690-900, Brazil – sequence: 2 givenname: Diogo M. surname: Freitas fullname: Freitas, Diogo M. email: diogom42@gmail.com organization: Instituto de Informática, Universidade Federal de Goiás, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, Goiás, 74690-900, Brazil – sequence: 3 givenname: Silvia R. surname: Vergilio fullname: Vergilio, Silvia R. email: silvia@inf.ufpr.br organization: Departamento de Informática, Universidade Federal do Paraná, Rua Cel. Francisco Heráclito Santos 100, Jardim das Américas, Curitiba, Paraná, 81531-980, Brazil – sequence: 4 givenname: Celso G. orcidid: 0000-0003-2553-8790 surname: Camilo-Junior fullname: Camilo-Junior, Celso G. email: celso@inf.ufg.br organization: Instituto de Informática, Universidade Federal de Goiás, Alameda Palmeiras, Quadra D, Câmpus Samambaia, Goiânia, Goiás, 74690-900, Brazil – sequence: 5 givenname: Rachel surname: Harrison fullname: Harrison, Rachel email: rachel.harrison@brookes.ac.uk organization: School of Engineering, Computing, and Maths, Oxford Brookes University, Wheatley Campus, OX33 1HX, Wheatley, Oxford, United Kingdom |
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| Keywords | Meta-heuristic algorithms Search-based fault localisation Systematic mapping |
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