bug fixing
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2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rama Rao Narvaneni ◽  
K. Suresh Babu

PurposeSoftware reliability growth models (SRGMs) are used to assess and predict reliability of a software system. Many of these models are effective in predicting future failures unless the software evolves.Design/methodology/approachThis objective of this paper is to identify the best path for rectifying the BFT (bug fixing time) and BFR (bug fixing rate). Moreover, the flexible software project has been examined while materializing the BFR. To enhance the BFR, the traceability of bug is lessened by the version tag virtue in every software deliverable component. The release time of software build is optimized with the utilization of mathematical optimization mechanisms like ‘software reliability growth’ and ‘non-homogeneous Poisson process methods.’FindingsIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.Originality/valueIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.


2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Fengcai Wen ◽  
Csaba Nagy ◽  
Michele Lanza ◽  
Gabriele Bavota

AbstractMost changes during software maintenance and evolution are not atomic changes, but rather the result of several related changes affecting different parts of the code. It may happen that developers omit needed changes, thus leaving a task partially unfinished, introducing technical debt or injecting bugs. We present a study investigating “quick remedy commits” performed by developers to implement changes omitted in previous commits. With quick remedy commits we refer to commits that (i) quickly follow a commit performed by the same developer, and (ii) aim at remedying issues introduced as the result of code changes omitted in the previous commit (e.g., fix references to code components that have been broken as a consequence of a rename refactoring) or simply improve the previously committed change (e.g., improve the name of a newly introduced variable). Through a manual analysis of 500 quick remedy commits, we define a taxonomy categorizing the types of changes that developers tend to omit. The taxonomy can (i) guide the development of tools aimed at detecting omitted changes and (ii) help researchers in identifying corner cases that must be properly handled. For example, one of the categories in our taxonomy groups the reverted commits, meaning changes that are undone in a subsequent commit. We show that not accounting for such commits when mining software repositories can undermine one’s findings. In particular, our results show that considering completely reverted commits when mining software repositories accounts, on average, for 0.07 and 0.27 noisy data points when dealing with two typical MSR data collection tasks (i.e., bug-fixing commits identification and refactoring operations mining, respectively).


2021 ◽  
Author(s):  
Wei Yuan ◽  
Yuan Xiong ◽  
Hailong Sun ◽  
Xudong Liu

Author(s):  
B. Luaphol ◽  
J. Polpinij ◽  
M. Kaneampornpan

<p>Bug reports contain essential information for fixing problems that occur in software. Many studies have proposed methods for automatic analysis of bug reports. One such task could affect the completion of software bug fixing, known as “bug dependency”. Although this problem was mentioned by many researches, most of them discussed about the related bugs but not really dealt with dependency issue in bug reports. One possible solution used for addressing this issue is to assemble all relevant/dependent bug reports together before analysis of the next processing stages. This study presents a method of assembling dependent bug reports. The main mechanism is called “threshold-based similarity analysis”, and the three similarity techniques of cosine similarity (CS) multi aspect TF (MATF), and BM25 are compared with feedback, precision and likelihood value. As the BM25 with the threshold as 0.5 gives the best results, it was used to compare with the state of the art method. The results show that our method increases precision and likelihood values by 12% and 12.4% respectively. Therefore, our results can be used to encourage developers to recognize all dependent bugs in the same problem domain.</p>


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