Exploring the usefulness of unlabelled test cases in software fault localization

Software testing run through the entire software development cycle, which consumes more than 50% of the development and maintenance effort. Fault localization is the most difficult and time-consuming task in software testing. Aimed at locating faults automatically in software, fault localization approach locates faults by running suitable test cases, so the quality of the test cases determines the effectiveness of fault localization. Therefore, the studies on test cases reduction approach for fault localization, automated fault-localization approach with high error-detection ability, and fault comprehension approach to help programmers understand the reason why a faulty statement cause failures have great significance for software testing and development. In this paper, the test cases reduction, fault localization, fault comprehension methods and their limitations are analyzed and summarized, and the future developing trend are discussed at last.

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Using Fine-Grained Test Cases for Improving Novice Program Fault Localization

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) ◽

10.1109/compsac48688.2020.00032 ◽

2020 ◽

Author(s):

Zheng Li ◽

Deli Yu ◽

Yonghao Wu ◽

Yong Liu

Keyword(s):

Fault Localization ◽

Test Cases ◽

Fine Grained

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Specification-guided Software Fault Localization for Autonomous Mobile Systems

2020 18th ACM-IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE) ◽

10.1109/memocode51338.2020.9315067 ◽

2020 ◽

Author(s):

Tomoya Yamaguchi ◽

Bardh Hoxha ◽

Danil Prokhorov ◽

Jyotirmoy V. Deshmukh

Keyword(s):

Fault Localization ◽

Mobile Systems ◽

Software Fault

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MuSim: Mutation-based Fault Localization Using Test Case Proximity

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194021500212 ◽

2021 ◽

Vol 31 (05) ◽

pp. 725-744

Author(s):

Arpita Dutta ◽

Amit Jha ◽

Rajib Mall

Keyword(s):

Fault Localization ◽

Test Case ◽

Test Cases ◽

Similarity Metrics ◽

Localization Technique ◽

Evaluation Metric

Fault localization techniques aim to localize faulty statements using the information gathered from both passed and failed test cases. We present a mutation-based fault localization technique called MuSim. MuSim identifies the faulty statement based on its computed proximity to different mutants. We study the performance of MuSim by using four different similarity metrics. To satisfactorily measure the effectiveness of our proposed approach, we present a new evaluation metric called Mut_Score. Based on this metric, on an average, MuSim is 33.21% more effective than existing fault localization techniques such as DStar, Tarantula, Crosstab, Ochiai.

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Statement spectrum with two dimensional eigenvalues for intelligent software fault localization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202931 ◽

2021 ◽

pp. 1-16

Author(s):

Shengbing Ren ◽

Xing Zuo ◽

Jun Chen ◽

Wenzhao Tan

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Rbf Neural Network ◽

Fault Localization ◽

Test Case ◽

Two Dimensional ◽

Intelligent Software ◽

Localization Model ◽

Software Fault ◽

Program Spectrum

The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.

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