A learning-to-rank based fault localization approach using likely invariants

Le, Tien-Duy B.; Lo, David; Goues, Claire Le; Grunske, Lars

doi:10.1145/2931037.2931049

Cited by 131 publications

(36 citation statements)

References 57 publications

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“…Jin and Orso (2013) used synthesized passing and failing executions to perform fault localization. Le et al (2015Le et al ( , 2016 utilized approaches to program spectra analysis to find suspicious words and invariant mining. Jones and Harrold (2005) implemented Tarantula approach of generating likelihood/suspicion for each statement of source code using the code entities executed bypassing and failing test cases.…”

Section: Non-ir-based Bug Localizationmentioning

confidence: 99%

“…These IR-based approaches, unlike some other spectrumbased approaches (Cleve and Zeller 2005;Dit et al 2013;Poshyvanyk et al 2013Poshyvanyk et al , 2007Liu et al 2005;Jin and Orso 2013;Le et al 2016Le et al , 2015Jones and Harrold 2005) that use runtime execution information to locate bugs, do not require running test cases. However, because they rely on the bug report content, the uneven quality of bug reports can be an impediment to their performance.…”

Section: Introductionmentioning

confidence: 99%

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On the classification of bug reports to improve bug localization

Fang

et al. 2021

Soft Comput

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Bug localization is the automated process of finding the possible faulty files in a software project. Bug localization allows developers to concentrate on vital files. Information retrieval (IR)-based approaches have been proposed to assist automatically identify software defects by using bug report information. However, some bug reports that are not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based reporting system can lead to false-positive results. In this paper, we propose a classification model for classifying a bug report as either uninformative or informative. Our approach helps to lower false positives and increase ranking performances by filtering uninformative information before running an IR-based bug location system. The model is based on implicit features learned from bug reports that use neural networks and explicit features defined manually. We test our proposed model on three open-source software projects that contain over 9000 bug reports. The results of the evaluation show that our model enhances the efficiency of a developed IR-based system in the trade-off between precision and recall. For implicit features, our tests with comparisons show that the LSTM network performs better than the CNN and multilayer perceptron with respect to the F-measurements. Combining both implicit and explicit features outperforms using only implicit features. Our classification model helps improve precision in bug localization tasks when precision is considered more important than recall.

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Section: Non-ir-based Bug Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the classification of bug reports to improve bug localization

Fang

et al. 2021

Soft Comput

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“…In 2017, we can see that the utilisation of Siemens suite programs has decreased with seven studies adopting it, primarily because the dataset is not considered sufficient anymore. Of recent, real-life programs from Defect4J repository are the trends in fault localisation research which were recently used by few studies [24,[74][75][76].…”

Section: Rq3 Which Datasets Are Used For Software Fault Localisation?mentioning

confidence: 99%

Software fault localisation: a systematic mapping study

et al. 2019

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Software fault localisation (SFL) is recognised to be one of the most tedious, costly, and critical activities in program debugging. Due to the increase in software complexity, there is a huge interest in advanced SFL techniques that aid software engineers in locating program bugs. This interest paves a way to the existence of a large amount of literature in the SFL research domain. This study aims to investigate the overall research productivity, demographics, and trends shaping the landscape of SFL research domain. The research also aims to classify existing fault localisation techniques and identify trends in the field of study. Accordingly, a systematic mapping study of 273 primary selected studies is conducted with the adoption of an evidence-based systematic methodology to ensure coverage of all relevant studies. The results of this systematic mapping study show that SFL research domain is gaining more attention since 2010, with an increasing number of publications per year. Three main research facets were identified, i.e. validation research, evaluation research, and solution research, with solution research type getting more attention. Hence, various contribution facets were identified as well. In totality, general demographics of SFL research domain were highlighted and discussed.

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“…However, contemporary evaluations mostly favour absolute measures [14], [13]. The most commonly adopted metrics are wasted effort, acc@n, and mean average precision [13], [15], [16], [7]. Consequently, we use these metrics to relatively assess the performance of spectrum based fault localisation against such simple test-tocode traceability .…”

Section: B Evaluation Metricsmentioning

confidence: 99%

“…To help developers in fault localisation, the research community has forwarded the spectrum based fault localisation [5], [6], [7], [8], [9]. Spectrum based fault localisation is an automated fault localisation technique that assigns a suspiciousness to each program element that indicates the likelihood of the program element to be faulty and , based on the suspiciousness, ranks the program elements with the aim to rank the faulty elements on the top.…”

Section: Introductionmentioning

confidence: 99%

Comparing Spectrum Based Fault Localisation Against Test-to-Code Traceability Links

Laghari

Dahri

Demeyer

2018

2018 International Conference on Frontiers of Information Technology (FIT)

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The recent shift towards automated software tests stimulated research interest in fault localisation. Fault localisation addresses the question which program elements need to be fixed to repair a failing test. The current state of the art in that field is named spectrum based fault localisation, which relies on dynamic coverage information from both failing and passing test cases to pinpoint the faulty program elements. This is in sharp contrast with the naïve approach which extracts traceability links between the test code and the program elements under test and enumerates those until the faulty element is found. In this paper we ask ourselves the question whether the state-of-the-art approach (spectrum based fault localisation) is so much better than the naïve approach (test-to-code traceability). We demonstrate on 178 defects from three representative projects in the recent Defects4J dataset that spectrum based fault localisation does not perform better than test-to-code traceability. This implies that future improvements in spectrum based fault localisation should also be compared against naïve approaches, such as test-to-code traceability.

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A learning-to-rank based fault localization approach using likely invariants

Cited by 131 publications

References 57 publications

On the classification of bug reports to improve bug localization

On the classification of bug reports to improve bug localization

Software fault localisation: a systematic mapping study

Comparing Spectrum Based Fault Localisation Against Test-to-Code Traceability Links

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