Does bug report summarization help in enhancing the accuracy of bug severity classification?

Kukkar, Ashima; Mohana, Rajni; Kumar, Yugal

doi:10.1016/j.procs.2020.03.345

Cited by 11 publications

(7 citation statements)

References 14 publications

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“… Naïve Bayes: NB classifier is used for bug severity prediction with PSO and ACO feature extraction techniques on Mozilla, Firefox, and Eclipse datasets. Good results are achieved for precision recall and F-measure [ 34 ]. Emotion similarity multinomial NB classifier is applied on Eclipse, GNU, JBoss, Mozilla, and Wireshark with average accuracy of 70.86%, 80.32%, 88.12%, 55.65%, and 41.63% with respective datasets [ 35 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Yousaf

Anwar

Imtiaz

et al. 2022

Computational Intelligence and Neuroscience

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Softwares are involved in all aspects of healthcare, such as booking appointments to software systems that are used for treatment and care of patients. Many vendors and consultants develop high quality software healthcare systems such as hospital management systems, medical electronic systems, and middle-ware softwares in medical devices. Internet of Things (IoT) medical devices are gaining attention and facilitate the people with new technology. The health condition of the patients are monitored by the IoT devices using sensors, specifically brain diseases such as Alzheimer, Parkinson’s, and Traumatic brain injury. Embedded software is present in IoT medical devices and the complexity of software increases day-by-day with the increase in the number and complexity of bugs in the devices. Bugs present in IoT medical devices can have severe consequences such as inaccurate records, circulatory suffering, and death in some cases along with delay in handling patients. There is a need to predict the impact of bugs (severe or nonsevere), especially in case of IoT medical devices due to their critical nature. This research proposes a hybrid bug severity prediction model using convolution neural network (CNN) and Harris Hawk optimization (HHO) based on an optimized hyperparameter of CNN with HHO. The dataset is created, that consists of the bugs present in healthcare systems and IoT medical devices, which is used for evaluation of the proposed model. A preprocessing technique on textual dataset is applied along with a feature extraction technique for CNN embedding layer. In HHO, we define the hyperparameter values of “Batch Size, Learning Rate, Activation Function, Optimizer Parameters, and Kernel Initializers,” before training the model. Hybrid model CNN-HHO is applied, and a 10-fold cross validation is performed for evaluation. Results indicate an accuracy of 96.21% with the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Yousaf

Anwar

Imtiaz

et al. 2022

Computational Intelligence and Neuroscience

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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%

“…The result showed the model achieved the F-measure ranging from 14% to 97%. Also, reference [14] proposed a new method to assign severity levels for bugs using swarm intelligence and machine learning. The optimum feature set was extracted from bug summery and trained Naive Bayes models to classify bug reports.…”

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%

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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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