Probabilistic Fault Localisation

Landsberg, David; Chockler, Hana; Kroening, Daniel

doi:10.1007/978-3-319-49052-6_5

Cited by 3 publications

(5 citation statements)

References 49 publications

(114 reference statements)

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“…We now discuss extensions of sfo. It is known that adding passing executions help in sbfl [4,5,[7][8][9][10][11][12], thus to develop a more effective fault localisation procedure we developed a second implementation sfo p (sfo with passing traces) that runs sfo and then adds passing test cases. To do this, after running sfo we call a SMT solver 20 times to find up to 20 new passing execution, where on each call if the vector found has new coverage properties (does not cover all the same uuts as some passing vector already computed) it is added to a set of passing vectors.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, the available sir benchmarks satisfy the third criterion, but not the second 4 . The software verification competition (sv-comp) benchmarks satisfy the second criterion, but almost never satisfy the third 5 . Furthermore, it is often difficult to obtain benchmarks from authors even when usable benchmarks do in fact exist.…”

Section: Setupmentioning

confidence: 99%

“…In this paper, we advance the state of the art in lightweight fault localisation by building on research in spectrum-based fault localisation (sbfl). sbfl is one of the most prominent areas of software fault localisation research, estimated to make up 35% of published work in the field to date [3], and has been demonstrated to be efficient and effective at finding faults [4][5][6][7][8][9][10][11][12]. The effectiveness relies on two factors, (1) the quality of the measure used to identify the lines of code that are suspected to be faulty, and (2) the quality of the test suite used.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness relies on two factors, (1) the quality of the measure used to identify the lines of code that are suspected to be faulty, and (2) the quality of the test suite used. Most research in the field has been focussed on finding improved measures [4][5][6][7][8][9][10][11][12], but there is a growing literature on how to improve the quality of test suites [13][14][15][16][17][18][19][20]. An outstanding problem in this field is to identify the properties that test suites should satisfy to improve fault localisation.…”

Section: Introductionmentioning

confidence: 99%

“…http://sir.unl.edu/portal/index.php 5. Benchmarks can be accessed at https://sv-comp.sosy-lab.org/2018/ 6.…”

mentioning

confidence: 99%

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Optimising Spectrum Based Fault Localisation for Single Fault Programs Using Specifications

Landsberg

Sun

Kroening

2018

Fundamental Approaches to Software Engineering

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Spectrum based fault localisation determines how suspicious a line of code is with respect to being faulty as a function of a given test suite. Outstanding problems include identifying properties that the test suite should satisfy in order to improve fault localisation effectiveness subject to a given measure, and developing methods that generate these test suites efficiently.We address these problems as follows. First, when single bug optimal measures are being used with a single-fault program, we identify a formal property that the test suite should satisfy in order to optimise fault localisation. Second, we introduce a new method which generates test data that satisfies this property. Finally, we empirically demonstrate the utility of our implementation at fault localisation on sv-comp benchmarks and the tcas program, demonstrating that test suites can be generated in almost a second with a fault identified after inspecting under 1% of the program.

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Section: Methodsmentioning

confidence: 99%

Section: Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…http://sir.unl.edu/portal/index.php 5. Benchmarks can be accessed at https://sv-comp.sosy-lab.org/2018/ 6.…”

mentioning

confidence: 99%

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Optimising Spectrum Based Fault Localisation for Single Fault Programs Using Specifications

Landsberg

Sun

Kroening

2018

Fundamental Approaches to Software Engineering

Self Cite

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Locating faults with program slicing: an empirical analysis

et al. 2021

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Statistical fault localization is an easily deployed technique for quickly determining candidates for faulty code locations. If a human programmer has to search the fault beyond the top candidate locations, though, more traditional techniques of following dependencies along dynamic slices may be better suited. In a large study of 457 bugs (369 single faults and 88 multiple faults) in 46 open source C programs, we compare the effectiveness of statistical fault localization against dynamic slicing. For single faults, we find that dynamic slicing was eight percentage points more effective than the best performing statistical debugging formula; for 66% of the bugs, dynamic slicing finds the fault earlier than the best performing statistical debugging formula. In our evaluation, dynamic slicing is more effective for programs with single fault, but statistical debugging performs better on multiple faults. Best results, however, are obtained by a hybrid approach: If programmers first examine at most the top five most suspicious locations from statistical debugging, and then switch to dynamic slices, on average, they will need to examine 15% (30 lines) of the code. These findings hold for 18 most effective statistical debugging formulas and our results are independent of the number of faults (i.e. single or multiple faults) and error type (i.e. artificial or real errors).

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