FPA-FL: Incorporating static fault-proneness analysis into statistical fault localization

Abstract

Despite the proven applicability of the statistical methods in automatic fault localization, these approaches are biased by data collected from different executions of the program. This biasness could result in unstable statistical models which may vary dependent on test data provided for trial executions of the program. To resolve the difficulty, in this article a new 'fault-proneness'-aware statistical approach based on Elastic-Net regression, namely FPA-FL is proposed. The main idea behind FPA-FL is to consider the static structure and the fault-proneness of the program statements in addition to their dynamic correlations with the program termination state. The grouping effect of FPA-FL is helpful for finding multiple faults and supporting scalability. To provide the context of failure, causeeffect chains of program faults are discovered. FPA-FL is evaluated from different viewpoints on well-known test suites. The results reveal high fault localization performance of our approach, compared with similar techniques in the literature.

A Framework for Improving Fault Localization Effectiveness Based on Fuzzy Expert System

“…Neelofar et al [72] used static analysis to categorize program statements into various classes and then adjusted the suspiciousness scores computed by a fault locator function according to each class's weight. Similarly, Feyzi and Parsa [60] also considered the static structure and the fault-proneness of code statements, and then proposed a new approach to improve the effectiveness based on Elastic-Net regression.…”

Mulr4FL: Effective Fault Localization of Evolution Software Based on Multivariate Logistic Regression Model

“…Besides, a number of SFL techniques improve fault localization efficiency by using a hybrid spectrum of program slice and execution coverage. Such as HSFal [43], IPSETFUL [44], FPA-FL [45], etc. A recent work by Chaleshtari et al [46] combines mutation techniques into fault localization with the help of program slicing, which aims to reduce the number of statements to be mutated.…”

An evaluation of pure spectrum‐based fault localization techniques for large‐scale software systems

“…Two works in this direction include those by Jiang and Su and Zhao et al that statically extract control flow graphs and use it to improve SBFL. The work by Feyzi and Parsa uses a static fault‐proneness analysis. de Souza et al used code hierarchy and integration coverage–based debugging techniques to provide better ranking of methods to be inspected.…”