Comparing fine-grained source code changes and code churn for bug prediction - A retrospective

2021 ◽  
Vol 46 (3) ◽  
pp. 21-23
Author(s):  
Martin Pinzger ◽  
Emanuel Giger ◽  
Harald C. Gall
Keyword(s):  
Source Code ◽  
Software Systems ◽  
Research Directions ◽  
Software Repositories ◽  
The Road ◽  
Fine Grained ◽  
Influential Paper ◽  
Code Changes ◽  
To Receive ◽  
Source Code Changes

More than two decades ago, researchers started to mine the data stored in software repositories to help software developers in making informed decisions for developing and testing software systems. Bug prediction was one of the most promising and popular research directions that uses the data stored in software repositories to predict the bug-proneness or number of bugs in source files. On that topic and as part of Emanuel's PhD studies, we submitted a paper with the title Comparing fine-grained source code changes and code churn for bug prediction [8] to the 8th Working Conference on Mining Software Engineering, held 2011 in beautiful Honolulu, Hawaii. Ten years later, it got selected as one of the finalists to receive the MSR 2021 Most Influential Paper Award. In the following, we provide a retrospective on our work, describing the road to publishing this paper, its impact in the field of bug prediction, and the road ahead.

Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model

2021 ◽  
Vol 135 ◽  
pp. 106566
Author(s):  
Lobna Ghadhab ◽  
Ilyes Jenhani ◽  
Mohamed Wiem Mkaouer ◽  
Montassar Ben Messaoud
Keyword(s):  
Language Model ◽  
Source Code ◽  
Fine Grained ◽  
Code Changes ◽  
Source Code Changes

A Novel Effort Measure Method for Effort-Aware Just-in-Time Software Defect Prediction

2021 ◽  
Vol 31 (08) ◽  
pp. 1145-1169
Author(s):  
Liqiong Chen ◽  
Shilong Song ◽  
Can Wang
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Systems ◽  
Support Vector ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Fine Grained ◽  
Software Defect ◽  
Code Changes ◽  
The Cost

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

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