Does the fault reside in a stack trace? Assisting crash localization by predicting crashing fault residence

Gu, Yongfeng; Xuan, Jifeng; Zhang, Hongyu; Zhang, Lanxin; Fan, Qingna; Xie, Xue‐Jun; Qian, Tieyun

doi:10.1016/j.jss.2018.11.004

Cited by 36 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this work, we employ a publicly available benchmark dataset collected by Gu et al [2] packages and applications. IO provides many input and output related classes, such as streams, readers, writers, and files, to simplify the development of IO functionality.…”

Section: A Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Zhao

Liu

Zhou

et al. 2021

Preprint

View full text Add to dashboard Cite

The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.

show abstract

Section: A Datasetmentioning

confidence: 99%

“…Researchers have recently begun to focus on the topic of crashing fault residence prediction. Gu et al [2] proposed to extract the features from the stack trace and faulty code for this task. They proposed an automatic method called CraTer for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Zhao

Liu

Zhou

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Gu et al [28] proposed an automatic approach, namely CraTer, which predicts whether a crashing fault resides in stack traces or not (referred to as predicting crashing fault residence ). However, our work combines dynamic analysis (using stack-trace information) with static backward dataflow analysis to identify the source statement.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Localization Tool for Null Pointer Exceptions

Duan

Jiang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Null pointer exceptions are common software faults, but they are dangerous and can cause a program to crash if they occur. In addition, it is hard to find them by simply running test cases. When a null pointer exception occurs, the stack trace stored by the Java runtime environment can help us to locate the cause of the exception. In this paper, firstly, we propose an automatically null pointer exception localization approach guided by stack trace, from the null pointer dereference to the null value assignment point. Secondly, an empirical study is designed to evaluate the effectiveness of the tool on eight open source projects. The experimental results show that the tool is effective in locating the null pointer exception. INDEX TERMS Null pointer exception, fault localization, stack trace.

show abstract

“…软件崩溃定位 (crash localization) 指的是根据已有的报错信息推测出崩溃根源的大概位置的过程 [5,43] . 更准确地讲, 给定一段引发崩溃的代码和相对应的崩溃报错信息, 崩溃定位旨在找到触发崩溃根源的位置.…”

Section: 崩溃定位的研究unclassified

“…由于准确的定位崩溃比较困难, Gu 等 [43] 提出了一种崩溃分离方法 CraTer, 该方法通过机器学习的方法预测崩溃的根源是否在栈踪迹中. 方法框架如图 4 所示, 方法分为训练阶段和部署阶段.…”

Section: 崩溃定位的研究unclassified

Progress on software crash research

Ping

Jia

et al. 2019

Sci. Sin.-Inf.

Self Cite

View full text Add to dashboard Cite

show abstract

Does the fault reside in a stack trace? Assisting crash localization by predicting crashing fault residence

Cited by 36 publications

References 13 publications

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set

An Automatic Localization Tool for Null Pointer Exceptions

Progress on software crash research

Contact Info

Product

Resources

About