The Multi-Slice Method of Program Slicing for Fault Location

Program slicing is a program analysis and understanding of technology. Sequence fault localization refers to the use of specific methods for faults in the program. Currently, the research program fault positioning is more and more people's attention and gets some results which is the more mainstream software fault localization method. Program slicing technique currently used to locate the fault procedures, which primarily to take advantage of dynamic slicing technique. Based on the full analysis of the advantages and disadvantages on the basis of previous work, we propose a flexible slicing rule and give a new method based on the slicing rule.

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Software fault localization based on program slicing spectrum

2012 34th International Conference on Software Engineering (ICSE) ◽

10.1109/icse.2012.6227049 ◽

2012 ◽

Cited By ~ 13

Author(s):

Wanzhi Wen

Keyword(s):

Fault Localization ◽

Program Slicing ◽

Software Fault

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Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/661 ◽

2019 ◽

Cited By ~ 2

Author(s):

Sofia Reis ◽

Rui Abreu ◽

Marcelo d'Amorim

Keyword(s):

Fault Diagnosis ◽

Diagnostic Accuracy ◽

Fault Localization ◽

Dynamic Slicing ◽

Software Fault ◽

Automated Software ◽

Comprehensive Study

Several approaches have been proposed to reduce debugging costs through automated software fault diagnosis. Dynamic Slicing (DS) and Spectrum-based Fault Localization (SFL) are popular fault diagnosis techniques and normally seen as complementary. This paper reports on a comprehensive study to reassess the effects of combining DS with SFL. With this combination, components that are often involved in failing but seldom in passing test runs could be located and their suspiciousness reduced. Results show that the DS-SFL combination, coined as Tandem-FL, improves the diagnostic accuracy up to 73.7% (13.4% on average). Furthermore, results indicate that the risk of missing faulty statements, which is a DS?s key limitation, is not high ? DS misses faulty statements in 9% of the 260 cases. To sum up, we found that the DS-SFL combination was practical and effective and encourage new SFL techniques to be evaluated against that optimization.

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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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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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