BugFix: A learning-based tool to assist developers in fixing bugs

Author

Jeffrey, Dennis ; Feng, Min ; Gupta, Neeraj ; Gupta, Neelam

Author_Institution

CSE Dept., Univ. of California, Riverside, CA

fYear

2009

fDate

17-19 May 2009

Firstpage

70

Lastpage

79

Abstract

We present a tool called BugFix that can assist developers in fixing program bugs. Our tool automatically analyzes the debugging situation at a statement and reports a prioritized list of relevant bug-fix suggestions that are likely to guide the developer to an appropriate fix at that statement. BugFix incorporates ideas from machine learning to automatically learn from new debugging situations and bug fixes over time. This enables more effective prediction of the most relevant bug-fix suggestions for newly-encountered debugging situations. The tool takes into account the static structure of a statement, the dynamic values used at that statement by both passing and failing runs, and the interesting value mapping pairs associated with that statement. We present a case study illustrating the efficacy of BugFix in helping developers to fix bugs.

Keywords

learning (artificial intelligence); program debugging; BugFix; learning-based tool; machine learning; program bugs; Computer bugs; Error correction; Failure analysis; Fault diagnosis; Machine learning; Programming; Robustness; Runtime; Software debugging; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Program Comprehension, 2009. ICPC '09. IEEE 17th International Conference on

Conference_Location

Vancouver, BC

ISSN

1092-8138

Print_ISBN

978-1-4244-3998-0

Electronic_ISBN

1092-8138

Type

conf

DOI

10.1109/ICPC.2009.5090029

Filename

5090029