Using Spectrum-Based Fault Localization for Test Case Grouping

2009 ◽  
Author(s):  
Martin Weiglhofer ◽  
Gordon Fraser ◽  
Franz Wotawa
Keyword(s):  
Fault Localization ◽  
Test Case

MuSim: Mutation-based Fault Localization Using Test Case Proximity

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.

Statement spectrum with two dimensional eigenvalues for intelligent software fault localization

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.

scholarly journals Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 28 (2) ◽  
pp. 101-109
Keyword(s):  
Simulated Annealing ◽  
Software Testing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Fault Localization ◽  
Good Effect ◽  
Test Case ◽  
Genetic Simulated Annealing Algorithm ◽  
Starting Point ◽  
Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

scholarly journals How well does test case prioritization integrate with statistical fault localization?

2012 ◽  
Vol 54 (7) ◽  
pp. 739-758 ◽  
Author(s):  
Bo Jiang ◽  
Zhenyu Zhang ◽  
W.K. Chan ◽  
T.H. Tse ◽  
Tsong Yueh Chen
Keyword(s):  
Fault Localization ◽  
Test Case ◽  
Test Case Prioritization

scholarly journals IETCR: An Information Entropy Based Test Case Reduction Strategy for Mutation-Based Fault Localization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 124297-124310
Author(s):  
Haifeng Wang ◽  
Bin Du ◽  
Jie He ◽  
Yong Liu ◽  
Xiang Chen
Keyword(s):  
Information Entropy ◽  
Fault Localization ◽  
Test Case ◽  
Reduction Strategy
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