- Title
- Does the fault reside in a stack trace? Assisting crash localization by predicting crashing fault residence
- Creator
- Gu, Yongfeng; Xuan, Jifeng; Zhang, Hongyu; Zhang, Lanxin; Fan, Qingna; Xie, Xiaoyuan; Qian, Tieyun
- Relation
- Journal of Systems and Software Vol. 148, Issue February 2019, p. 88-104
- Publisher Link
- http://dx.doi.org/10.1016/j.jss.2018.11.004
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2019
- Description
- Given a stack trace reported at the time of software crash, crash localization aims to pinpoint the root cause of the crash. Crash localization is known as a time-consuming and labor-intensive task. Without tool support, developers have to spend tedious manual effort examining a large amount of source code based on their experience. In this paper, we propose an automatic approach, namely CraTer, which predicts whether a crashing fault resides in stack traces or not (referred to as predicting crashing fault residence). We extract 89 features from stack traces and source code to train a predictive model based on known crashes. We then use the model to predict the residence of newly-submitted crashes. CraTer can reduce the search space for crashing faults and help prioritize crash localization efforts. Experimental results on crashes of seven real-world projects demonstrate that CraTer can achieve an average accuracy of over 92%.
- Subject
- crash localization; stack trace; predictive model; crashing fault residence
- Identifier
- http://hdl.handle.net/1959.13/1467153
- Identifier
- uon:47761
- Identifier
- ISSN:0164-1212
- Language
- eng
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