Title: Statistical bug localisation by supervised clustering of program predicates

Authors: Farid Feyzi; Saeed Parsa; Esmaeel Nikravan

Addresses: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran ' School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran ' School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract: Regarding the fact that the majority of faults may be revealed as joint effect of program predicates on each other, a new method for localising complex bugs of programs is proposed in this article. The presented approach attempts to identify and select groups of interdependent predicates which altogether may affect the program failure. To find these groups, we suggest the use of a supervised algorithm that is based on penalised logistic regression analysis. To provide the failure context, faulty sub-paths are recognised as sequences of fault relevant predicates. Estimating the grouping effect of program predicates on the failure helps programmers in the multiple-bug setting. Several case studies have been designed to evaluate the proposed approach on well-known test suites. The evaluations show that our method produces more precise results compared with prior fault localisation techniques.

Keywords: fault localisation; statistical debugging; supervised clustering; grouping effect.

DOI: 10.1504/IJISCM.2018.094605

International Journal of Information Systems and Change Management, 2018 Vol.10 No.2, pp.190 - 206

Received: 18 Sep 2017
Accepted: 10 Jun 2018
Published online: 07 Sep 2018 *

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