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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Rai, Deepti
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A simplified predictive framework for cost evaluation to fault assessment using machine learning Rai, Deepti; Prashant, Jyothi Arcot
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7027-7036

Abstract

Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.
Insights of effectivity analysis of learning-based approaches towards software defect prediction Rai, Deepti; Arcot Prashant, Jyothi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1916-1927

Abstract

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.
A novel approach to enhancing software quality assurance through early detection and prevention of software faults Rai, Deepti; Prashant, Jyothi Arcot
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp894-906

Abstract

The current manuscript presents a predictive mechanism towards analyzing software defects by developing a line-level fault prediction technique. Current methodologies rely on customized attributes and overlook the sophisticated structural and semantic characteristics inherent in programming languages. This oversight often led to suboptimal defect identification, as code defects are intricately scrambled with their contextual environment. Moreover, conventional software defect prediction (SDP) strategies, typically focusing on larger code units such as modules or classes, impede precise error localization. To address these challenges, this study proposes an automated scheme utilizing a recurrent neural network (RNN) with an attention layer to analyze line-level quantifiers within the code, such as the number of pairwise operations and single operand operators. The efficacy of this learning-driven scheme is validated through comprehensive experiments conducted on several C++ programs. The experimental results demonstrate a 95.8% recall, 83.12% precision, and 90.35% accuracy in identifying fault-prone lines within a testing dataset. These outcomes confirm the effectiveness of proposed SDP scheme in accurately identifying the defects and highlighting its inter-project capabilities, exhibiting the model's adaptability across different software projects.
Co-Authors Arcot Prashant, Jyothi Prashant, Jyothi Arcot