Mining defect reports for predicting software maintenance effort

Jindal, Rajni; Malhotra, Ruchika; Jain, A. C.

doi:10.1109/icacci.2015.7275620

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…The results of the study are unique as only work by Jindal et al (2015) developed ME based SBC models. Studies in literature have not explored the development of CI based or combined approach based SBC models.…”

Section: Discussionmentioning

confidence: 99%

“…Table 1 lists some of these key studies and their characteristics (features used, SBC criteria, labels, datasets and evaluation measures used). Jindal et al (2015) have successfully attempted categorization of software bugs on the basis of ME required to correct them. However, there is a huge literature gap when it comes to categorization of software bugs into various levels on the basis of their CI.…”

Section: Software Bug Categorizationmentioning

confidence: 99%

“…those requiring low maintainability effort and the others requiring high maintainability effort. Jindal et al (2015) has ascertained the levels of ME required to correct a bug by creating ordinal variables with equal sized binning. Similar to these studies we divide our dependent variable into three categories "low", "moderate" and "high".…”

Section: Software Maintenance Effort Estimationmentioning

confidence: 99%

“…We convert these continuous variables into ordinal variables by binning the values into three equal sized categories according to their empirical distribution (Sentas et al 2005). Certain previous studies have also followed this approach of creating ordinal values by binning into equal sized bins (Balogh et al 2012;Jindal et al 2015). The three categories formed in this study were "low", "moderate" and "high".…”

Section: Overview Of Frameworkmentioning

confidence: 99%

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A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

Malhotra

Khanna

2022

International Journal of Information Retrieval Research

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Software practitioners often strive to achieve a “bug-free” software, though, it is a myth. Software Bug Categorization (SBC) models, which assigns levels (viz. “low”, “moderate” or “high”) to a software bug aid effective bug management. They assist in allocation of proper maintenance resources for bug elimination to improve software quality. This study proposes the development of SBC models that allocate levels on the basis of three software bug aspects i.e., maintenance effort required to correct a bug, its change impact and the combined effect of both of these. In order to develop SBC models, we use text mining approach, which extracts relevant features from bug descriptions and relates these features with different software bug levels. The results of the study indicate that the categorization of software bugs in accordance with maintenance effort and change impact is possible. Furthermore, the combined approach SBC models were also found to be effective.

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Section: Discussionmentioning

confidence: 99%

Section: Software Bug Categorizationmentioning

confidence: 99%

Section: Software Maintenance Effort Estimationmentioning

confidence: 99%

Section: Overview Of Frameworkmentioning

confidence: 99%

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A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

Malhotra

Khanna

2022

International Journal of Information Retrieval Research

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“…In 2015, Rajni et al conducted data mining on the attributes of existing Andriod software defect report by using MMLR method and based on DCRS software defect tracking system. The method optimized the characteristics based on IG method and obtained the description document of software defect report [85]. At the same time; they describe the current research classification of software defect report in the relevant work section.…”

Section: Feature Selection Algorithm In Quantitative Analysismentioning

confidence: 99%

Software Defect Data Mining: A Survey of Severity Analysis

Liu¹

2019

JSW

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Open source software USES software defect tracking system, which can effectively manage the related information of software defects, and build software defect data warehouse in the form of defect report. The severity attribute of software defect report can determine the important indicators such as the repairers, solving time and repairing rate of software defect. Much research on software defects focuses on severity analysis. In order to evaluate the work in the field of severity analysis, this paper reviews the existing studies. In particular, this paper introduces the main methods of severity study, and expounds the statistical characteristics analysis of severity attribute in software defect report data set. According to the research status, the research of severity analysis is divided into qualitative analysis and quantitative analysis, and analyzed in detail. At the same time, each part is empirically analyzed based on data from the Mozilla project and Eclipse project datasets. On this basis, this paper summarizes the existing work of severity analysis of defect report, and points out some possible problems in the work.

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Severity Prediction of Software Vulnerabilities Using Textual Data

Malhotra

Vidushi

2020

Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications

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Mining defect reports for predicting software maintenance effort

Cited by 6 publications

References 28 publications

A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

Software Defect Data Mining: A Survey of Severity Analysis

Severity Prediction of Software Vulnerabilities Using Textual Data

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