An empirical study of software entropy based bug prediction using machine learning

Kaur, Amandeep; Kaur, Kamaldeep; Chopra, Deepti

doi:10.1007/s13198-016-0479-2

Cited by 22 publications

(8 citation statements)

References 34 publications

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“…Dos Santos et al [92] suggested a sampling-based approach to extract source code metrics to train defect prediction models. Kaur et al [154] suggested an approach to fetch entropy of change metrics. Bowes et al [51] introduced a novel set of metrics constructed in terms of mutants and the test cases that cover and detect them.…”

Section: Data Labelingmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

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Context:The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and vulnerabilities detection. A large number of studies poses challenges to the community to understand the current landscape. Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we carried out an extensive literature search and identified 364 primary studies published between 2002 and 2021. We summarize our observations and findings with the help of the identified studies. Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task, and summarize the employed machine learning techniques. Additionally, we collate a comprehensive list of available datasets and tools useable in this context. Finally, we summarize the perceived challenges in this area that include availability of standard datasets, reproducibility and replicability, and hardware resources. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software maintenance tools; Software post-development issues; Maintaining software; • Computing methodologies → Machine learning.

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Section: Data Labelingmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The viability of error detection using artificial intelligence has already been demonstrated in many previous works (Delphine Immaculate et al, 2019;Hammouri et al, 2018;Puranik et al, 2016;Kaur and Chopra, 2016;Tóth et al, 2016;Melo et al, 2019). However, these solutions are used to predict any kinds of errors that may prevent the program from functioning properly, and not only vulnerabilities.…”

Section: Related Workmentioning

confidence: 99%

Improving Vulnerability Prediction of JavaScript Functions using Process Metrics

Viszkok

Hegedűs

Ferenć

2021

Proceedings of the 16th International Conference on Software Technologies

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Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building on previous results in predicting vulnerabilities at the level of functions in JavaScript code using the well-known static source code metrics. In this work, we propose to include several so-called process metrics (e.g., code churn, number of developers modifying a file, or the age of the changed source code) into the set of features, and examine how they affect the performance of the function-level JavaScript vulnerability prediction models. We can confirm that process metrics significantly improve the prediction power of such models. On average, we observed a 8.4% improvement in terms of F-measure (from 0.764 to 0.848), 3.5% improvement in terms of precision (from 0.953 to 0.988) and a 6.3% improvement in terms of recall (from 0.697 to 0.760).

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“…Kaur et al [13] proposed the metrics obtained by means of entropy of variations to evaluate five machine learning systems aimed at envisaging faults.…”

Section: Review Of Literaturementioning

confidence: 99%

Envisaging Bugs by Means of Entropy Measures

Munde

2020

Information and Communication Technology for Intelligent Systems

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There are numerous methods for envisaging bugs in software practices. A widespread method for bug estimation is applying entropy of alterations as recommended by Hassan [5]. In the existing literature, investigators have recommended and executed a surplus of bug extrapolation methodologies, which change in relation to precision, difficulty and the input files they need, nonetheless actually rarely any of them has anticipated the amount of bugs in the software established on the entropy. A standard for bug estimation is showed, in the way of a widely accessible data set comprising of numerous software techniques, and a huge divergence of the comprehensive and analytical ability of familiar bug estimation methods, collectively with innovative methods is offered. Based on the outcomes, the working and constancy of the methods with regard to our standard are examined and a number of understandings on bug estimation models are determined. In the present communication, we will acquire a method by taking into concern the amount of bugs logged in numerous announcements of Bugzilla software and compute the distinctive measures of entropy, namely Shannon entropy [18] and Kapur entropy [9] for the variations in several software updates in these time intervals. Eventually, this work has the possibility to shed light on how to utilize simple linear regression to envisage the bugs which are still impending.

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An empirical study of software entropy based bug prediction using machine learning

Cited by 22 publications

References 34 publications

A Survey on Machine Learning Techniques for Source Code Analysis

A Survey on Machine Learning Techniques for Source Code Analysis

Improving Vulnerability Prediction of JavaScript Functions using Process Metrics

Envisaging Bugs by Means of Entropy Measures

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