Study of Information Retrieval and Machine Learning-Based Software Bug Localization Models

被引：0
|
作者：
Tamanna [1 ]
Sangwan, Om Prakash [1 ]
机构：
[1] Guru Jambheshwar Univ Sci & Technol, Dept Comp Sci & Engn, Hisar, Haryana, India
来源：
ADVANCES IN COMPUTING AND INTELLIGENT SYSTEMS, ICACM 2019 | 2020年
关键词：
Vector space model; GLOVE; Word embedding; Software bug localization (SBL) etc;
D O I：
10.1007/978-981-15-0222-4_47
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Software bug localization (SBL) is a process of finding out the location of bug that causes the failure of some functionality in the application. There are many different methods of performing SBL like analysing of execution traces, information retrieval and manual debugging. Information retrieval (IR) based models works as same as simple search query model in which bug report is taken as query. In this paper, we perform an empirical study for verifying the effectiveness of VSM. Based on TFIDF modelling, the results are experimented on four datasets and evaluated with TOPK, MAP and MRR metrics. In addition to this, review of existing machine learning and deep learning-based SBL models are also presented because of their effective power and improved results in localization accuracy.
引用
收藏
页码：503 / 510
页数：8
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