Statistical bug localisation by supervised clustering of program predicates

Farid Feyzi, Saeed Parsa and Esmaeel Nikravan

International Journal of Information Systems and Change Management, 2018, vol. 10, issue 2, 190-206

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. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=94605 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijiscm:v:10:y:2018:i:2:p:190-206

Access Statistics for this article

More articles in International Journal of Information Systems and Change Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().