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ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 13:40 - 14:00 at Cortez 2&3 - Systems and Localization Chair(s): Tegawendé F. Bissyandé

Program debugging is a time-consuming task, and researchers have proposed different kinds of automatic fault localization techniques to mitigate the burden of manual debugging. Among these techniques, two popular families are spectrum-based fault localization (SBFL) and statistical debugging (SD), both localizing faults by collecting statistical information at runtime. Though the ideas are similar, the two families have been developed independently and their combinations have not been systematically explored.

In this paper we perform a systematical empirical study on the combination of SBFL and SD. We first build a unified model of the two techniques, and systematically explore four types of variations, different predicates, different risk evaluation formulas, different granularities of data collection, and different methods of combining suspicious scores.

Our study leads to several findings. First, most of the effectiveness of the combined approach contributed by a simple type of predicates: branch conditions. Second, the risk evaluation formulas of SBFL significantly outperform that of SD. Third, fine-grained data collection significantly outperforms coarse-grained data collection with a little extra execution overhead. Fourth, a linear combination of SBFL and SD predicates outperforms both individual approaches.

According to our empirical study, we propose a new fault localization approach, PREDFL (Predicate-based Fault Localization), with the best configuration for each dimension under the unified model. Then, we explore its complementarity to existing techniques by integrating PREDFL with a state-of-the-art fault localization framework. The experimental results show that PREDFL can further improve the effectiveness of state-of-the-art fault localization techniques. More concretely, integrating PREDFL results in an up to 20.8% improvement w.r.t the faults successfully located at Top-1, which reveals that PREDFL complements existing techniques.

Wed 13 Nov

13:40 - 15:20: Papers - Systems and Localization at Cortez 2&3
Chair(s): Tegawendé F. BissyandéSnT, University of Luxembourg
ase-2019-papers13:40 - 14:00
Combining Spectrum-Based Fault Localization and Statistical Debugging: An Empirical Study
Jiajun JiangPeking University, Ran WangPeking University, Yingfei XiongPeking University, Xiangping ChenSun Yat-sen University, Lu ZhangPeking University
ase-2019-papers14:00 - 14:20
SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces
Tarannum Shaila ZamanUniversity of Kentucky, Xue HanUniversity of Kentucky, Tingting YuUniversity of Kentucky
Pre-print File Attached
ase-2019-papers14:20 - 14:40
Root Cause Localization for Unreproducible Builds via Causality Analysis over System Call Tracing
Zhilei RenDalian University of Technology, Changlin LiuCase Western Reserve University, Xusheng XiaoCase Western Reserve University, He JiangSchool of Software, Dalian University of Technology, Tao XiePeking University
ase-2019-Industry-Showcase14:40 - 15:00
PTracer: A Linux Kernel Patch Trace Bot
Yang WenZTE Corporation, Jicheng CaoZTE Corporation, Shengyu ChengZTE Corporation
ase-2019-Demonstrations15:00 - 15:10
Pangolin: An SFL-based Toolset for Feature Localization
Bruno Miguel Sotto-Mayor de Castro MachadoIST, University of Lisbon, Alexandre PerezPalo Alto Research Center, Rui AbreuInstituto Superior Técnico, U. Lisboa & INESC-ID
ase-2019-Demonstrations15:10 - 15:20
SiMPOSE - Configurable N-Way Program Merging Strategies for Superimposition-based Analysis of Variant-Rich Software
Dennis ReulingSoftware Engineering Group, University of Siegen, Udo KelterSoftware Engineering Group, University of Siegen, Sebastian RulandTU Darmstadt, Real-time Systems Lab, Malte LochauTU Darmstadt
Pre-print Media Attached File Attached