Prediction &amp; Assessment of Change Prone Classes Using Statistical &amp; Machine Learning Techniques

Malhotra, Ruchika; Jangra, Ravi

doi:10.3745/jips.04.0013

Cited by 11 publications

(5 citation statements)

References 22 publications

(45 reference statements)

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“…The study was conducted on the CK metrics in addition to dynamic coupling metrics. Malhotra and Jangra (2017) have studied the prediction of change-proneness of classes using object-oriented metrics. The authors conducted 10 machine learning techniques and compared the performance of the resulting models with a statistical model.…”

Section: Related Workmentioning

confidence: 99%

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

A Shatnawi

2023

IJIKM

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Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

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Section: Related Workmentioning

confidence: 99%

Predicting Software Change-Proneness From Software Evolution Using Machine Learning Methods

A Shatnawi

2023

IJIKM

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“…Xiao et al [344] utilized a Continuous Integration (ci) dataset and Pradel and Sen [249] generated a synthetic dataset. Apart from using the existing datasets, some other studies prepared their own datasets by utilizing various GitHub projects [4,131,209,210,296] including Apache [51,87,183], Eclipse [87,369] and Mozilla [159,206] projects, or industrial data [51]. [213].…”

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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“…Sunmin Lee and Nammee Moon proposed a location recognition system for users based on WiFi data (BSSID, RSSI) using Random Forest. In order to improve the accuracy, a radio Map was made to store BSSID efficiently and the filtered values were used as the learning data of Random Forest (Malhotra and Jangra 2017). Experiment results showed that the proposed algorithm had better accuracy, averaging 6.04% higher than the RSSI-based Random Forest location recognition.…”

Section: Related Workmentioning

confidence: 99%