Methods and measures for statistical fault localisation

Landsberg, D

Abstract:: Fault localisation is the process of finding the causes of a given error, and is one of the most costly elements of software development. One of the most efficient approaches to fault localisation appeals to statistical methods. These methods are characterised by their ability to estimate how faulty a program artefact is as a function of statistical information about a given program and test suite. However, the major problem facing statistical approaches is their effectiveness -- par...
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APA Style

Landsberg, D. (2016). Methods and measures for statistical fault localisation [PhD thesis]. University of Oxford.

MLA Style

Landsberg, D. Methods and Measures for Statistical Fault Localisation. University of Oxford, 2016.

Chicago Style

Landsberg, D. 2016. “Methods and Measures for Statistical Fault Localisation.” PhD thesis, University of Oxford.
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Files:: Landsberg_2016_Methods_and_measures.pdf

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Authors

+ Landsberg, D More by this author

Division:: MPLS
Department:: Computer Science
Department:: Oxford University
Role:: Author

Contributors

+ Kroening, D

Role:: Supervisor

+ Chockler, H

Role:: Supervisor

Type of award:: DPhil
Level of award:: Doctoral
Awarding institution:: University of Oxford

Language:: English
Keywords:: Error Explanation

Debugging

Fault Localization

Verification
Subjects:: Computer science
UUID:: uuid:cf737e06-9f12-44fa-94d2-a8d247ad808e
Deposit date:: 2017-04-08

Related item:: Evaluation of Measures for Statistical Fault Localisation and an Optimising Scheme
Description:: Statistical Fault Localisation (SFL) is a widely used method for localizing faults in software. SFL gathers coverage details of passed and failed executions over a faulty program and then uses a measure to assign a degree of suspiciousness to each of a chosen set of program entities (statements, predicates, etc.) in that program. The program entities are then inspected by the engineer in descending order of suspiciousness until the bug is found. The effectiveness of this process relies on the quality of the suspiciousness measure. In this paper, we compare 157 measures, 95 of which are new to SFL and borrowed from other branches of science and philosophy. We also present a new measure optimiser Lexg, which optimises a given measure g according to a criterion of single bug optimality. An experimental comparison on benchmarks from the Software-artifact Infrastructure Repository (SIR) indicates that many of the new measures perform competitively with the established ones. Furthermore, the large-scale comparison reveals that the new measures LexOchiai and Pattern-Similarity perform best overall.
Related item:: Probabilistic Fault Localisatization
Description:: Efficient fault localisation is becoming increasingly importantas software grows in size and complexity. In this paper we present anew formal framework, denoted probabilistic fault localisation (pfl),and compare it to the established framework of spectrum based faultlocalisation (sbfl). We formally prove that pfl satisfies some desirableproperties which sbfl does not, empirically demonstrate that pfl issignificantly more effective at finding faults than all known sbfl measuresin large scale experimentation, and show pfl has comparable efficiency.Results show that the user investigates 37% more code (and finds a faultimmediately in 27% fewer cases) when using the best performing sbflmeasures, compared to the pfl framework. Furthermore, we show that itis theoretically impossible to design strictly rational sbfl measures thatoutperform pfl techniques on a large set of benchmarks.

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Copyright holder:: Landsberg, D

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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Methods and measures for statistical fault localisation

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Methods and measures for statistical fault localisation

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