Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context

Kumari, Madhu; Singh, Ujjawal Kumar; Sharma, Meera

doi:10.1007/978-3-030-58817-5_66

Cited by 4 publications

(4 citation statements)

References 17 publications

(9 reference statements)

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“…There are models that predict bug severity using textual description in bug reports [7,8,9,10,11,12,13,14]. Reference [7] carried out a comparison between the newly proposed bug and already existing bugs in the bug database using BM25-based document similarity function and then assigned the severity for the new reports based on the similarity calculated.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [12] used shannon entropy measure to calculate uncertainty of bug summary. They trained classifiers using bug priority, along with summary weight and entropy to predict severity of bugs of new projects, which do not have history records.…”

Section: Related Workmentioning

confidence: 99%

“…In such a skewed learning environment the testing sample may also contain the same distribution as the training sample. However, many of the existing bug severity prediction models trained from history data are nether mention the distribution of the datasets used to train the models, nor acknowledged this issue [7,8,9,10,11,12,13,14]. This implies that they assumed the underlying data distribution is balanced, which may not essentially be true in all real datasets such as I use in this project.…”

Section: Introductionmentioning

confidence: 98%

“…Hence, the objective of this project is to develop bug severity prediction models from imbalanced learning environments and test them properly using an appropriate metric. Many models developed in the literature [7,8,9,10,11,12,13,14] used the accuracy--correctly classified instances vs. the total number of instances presented--to evaluate the prediction quality in skewed learning environments [4,5]. However, the accuracy would not provide fair judgment on the quality of models trained from unevenly distributed datasets like used in this project.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bug Severity Prediction using Keywords in Imbalanced Learning Environment

Ekanayake¹

2021

IJITCS

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Reported bugs of software systems are classified into different severity levels before fixing them. The number of bug reports may not be equally distributed according to the severity levels of bugs. However, most of the severity prediction models developed in the literature assumed that the underlying data distribution is evenly distributed, which may not correct at all instances and hence, the aim of this study is to develop bug classification models from unevenly distributed datasets and tested them accordingly. To that end first, the topics or keywords of developer descriptions of bug reports are extracted using Rapid Keyword Extraction (RAKE) algorithm and then transferred them into numerical attributes, which combined with severity levels constructs datasets. These datasets are used to build classification models; Naïve Bayes, Logistic Regression, and Decision Tree Learner algorithms. The models’ prediction quality is measured using Area Under Recursive Operative Characteristics Curves (AUC) as the models learnt from more skewed environments. According to the results, the prediction quality of the Logistics Regression model is 0.65 AUC whereas the other two models recorded maximum 0.60 AUC. Though the datasets contain comparatively less number of instances from the high severity classes; Blocking and High, the Logistic Regression models predict the two classes with a decent AUC value of 0.65 AUC. Hence, this projects shows that the models can be trained from highly skewed datasets so that the models prediction quality is equally well over all the classes regardless of number of instances representing the class. Further, this project emphasizes that the models should be evaluated using the appropriate metrics when the models are trained from imbalance learning environments. Also, this work uncovers that the Logistic Regression model is also capable of classifying documents as Naïve Bayes, which is well known for this task.

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