Publication Type

Conference Paper

Version

acceptedVersion

Publication Date

9-2018

Abstract

Dynamic slicing is a common way of identifying the root cause when a program fault is revealed. With the dynamic slicing technique, the programmers can follow data and control flow along the program execution trace to the root cause. However, the technique usually fails to work on omission bugs, i.e., the faults which are caused by missing executing some code. In many cases, dynamic slicing over-skips the root cause when an omission bug happens, leading the debugging process to a dead end. In this work, we conduct an empirical study on the omission bugs in the Defects4J bug repository. Our study shows that (1) omission bugs are prevalent (46.4%) among all the studied bugs; (2) there are repeating patterns on causes and fixes of the omission bugs; (3) the patterns of fixing omission bugs serve as a strong hint to break the slicing dead end. Based on our findings, we train a neural network model on the omission bugs in Defects4J repository to recommend where to approach when slicing can no long work. We conduct an experiment by applying our approach on 3193 mutated omission bugs which slicing fails to locate. The results show that our approach outperforms random benchmark on breaking the dead end and localizing the mutated omission bugs (63.8% over 2.8%).

Keywords

debugging, program slice, omission error, empirical study

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018), Corum, Montpellier, France, September 3-7

Identifier

10.1145/3238147.3238163

Publisher

IEEE

City or Country

Corum, Montpellier, France

Additional URL

https://doi.org/10.1145/3238147.3238163

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