Using likely invariants for automated software fault localization

Sahoo, Sanjaya Kumar; Criswell, John; Geigle, Chase; Adve, Vikram

doi:10.1145/2490301.2451131

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…Real‐life programs such as grep was utilised by 11 studies. Moreover, real‐time systems and programs such as helicopter control system, BPEL programs, VLC, Firework, MYSQL database, and Wireshark were used as well [56, 57, 64–66]. Hence, from 2014–2016, Siemens suite programs were considerably used by many studies with few relevant studies utilising the others.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Software fault localisation: a systematic mapping study

et al. 2019

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“…N CF refers to the number of times a statement is covered by failed test cases. Obviously, a higher N CF implies a higher number of failed test cases executing the statement, which in turn results in a larger possibility of the bug being found in the statement [14]. Therefore, N CF should be directly proportional to the suspiciousness of the statement.…”

Section: The Role Of Each Notation In the Spectrum Informationmentioning

confidence: 99%

“…Crosstab is a statistical localization method that creates a cross tabulation for each executable statement with the spectrum information to obtain the chi-square statistics and contingency correlation coefficient [14]. Its formula is shown in Equation ( 8):…”

Section: Crosstabmentioning

confidence: 99%

“…Wong et al [6] proposed DStar based on the high consistency of N CF and fault localization target. Reference [14] explained the meaning of each notation in the spectrum information and analyzed the role of N CF . Furthermore, it has been proven that N CS is inversely proportional to the suspiciousness of the statement, which indirectly confirms the role of N CF .…”

Section: Related Workmentioning

confidence: 99%

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Normalization of Notation NCF for Improving Fault Localization

Li¹

2019

IJPE

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In view of the importance and the high cost of effective software fault localization, how to improve the effectiveness of the software fault localization has become an important and persistent issue in software engineering. Featuring simple operation and high popularity, the spectrum-based fault localization technique obtains program spectrum information by executing test cases on the program and then calculates the suspiciousness of each statement, which provides a basis for the programmers to debug. This paper proposed CFNorm, a new fault localization parameter, which can be obtained by processing the column data of the spectrum information matrix. CFNorm emphasizes and amplifies the role of NCF (the number of times a statement is executed by failed test cases) to optimize the traditional fault localization technique for better fault localization. Three fault localization techniques were selected to be used in an experiment involving 111 versions of Siemens Suite. The results showed that the effectiveness of fault localization was significantly improved with the increase in the weight of CFNorm over a certain range.

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“…In the first part, we extract the features of statements in the program under testing. First, we execute each test case and construct its execution impact graph with the open source software giri [40]. Giri was originally a dynamic program slice tool and is currently modified by us.…”

Section: Structure Of the Automated Prediction Toolmentioning

confidence: 99%

A Model for Predicting Statement Mutation Scores

2019

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A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing is also computationally expensive. To solve this problem, predictive mutation testing builds a classification model to predict the test result of each mutant. However, the existing predictive mutation testing methods only can be used to estimate the overall mutation scores of object-oriented programs. To overcome the shortcomings of the existing methods, we propose a new method to directly predict the mutation score for each statement in process-oriented programs. Compared with the existing predictive mutation testing methods, our method uses more dynamic program execution features, which more adequately reflect dynamic dependency relationships among the statements and more accurately reflects information propagation during the execution of test cases. By comparing the prediction effects of logistic regression, artificial neural network, random forest, support vector machine, and symbolic regression, we finally decide to use a single hidden layer feedforward neural network as the predictive model to predict the statement mutation scores. In our two experiments, the mean absolute errors between the statement mutation scores predicted by the neural network and the real statement mutation scores both approximately reach 0.12.

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Using likely invariants for automated software fault localization

Cited by 8 publications

References 37 publications

Software fault localisation: a systematic mapping study

Software fault localisation: a systematic mapping study

Normalization of Notation NCF for Improving Fault Localization

A Model for Predicting Statement Mutation Scores

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