Software fault prediction using Whale algorithm with genetics algorithm

Alsghaier, Hiba; Akour, Mohammed

doi:10.1002/spe.2941

Cited by 14 publications

(4 citation statements)

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“…The datasets cover 14 distinct software development projects based mainly on three programming languages: C, C++, and Java. The nature of those software systems from the same organization was also different, probably with distinct architectures and design patterns, since some were related to, for example, spacecraft instruments (CM1), a storage management system for receiving and processing ground data (KC1, KC2, and KC3), a combustion experiment of a space shuttle (MC1), a video guidance system (MC2), a zero-gravity experiment related to combustion (MW1), flight software from an earth-orbiting satellite (PC1, PC2, and PC4), s dynamic simulator for attitude control systems (PC2), and a cockpit security increase system (PC5) [59,60]. Finally, the original study used a dataset from a single NASA project, which the present one seeks to extend.…”

Section: Datasetmentioning

confidence: 99%

Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training

Nascimento,

Shimanuki,

Dias

2024

Applied Sciences

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As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models’ in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach’s applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort.

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Section: Datasetmentioning

confidence: 99%

Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training

Nascimento,

Shimanuki,

Dias

2024

Applied Sciences

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“…The numerical results demonstrate that the proposed approach performs better on relatively large-scale complex problems. Alsghaier et al [138] developed an approach by integrating a genetic algorithm with a SVM classifier and WOA for software fault prediction. The results indicated that the developed approach was suitable for large and small-scale datasets.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…Results showed that IsBMFO, followed by support vector machine (SVM) classifcation, outperformed other models for the SDP problem, with an average G-mean of 78%. Alsghaier and Akour [60] developed an approach for software fault prediction by combining the genetics algorithm (GA) with the SVM classifer and WOA. It was then applied to 24 datasets, and the results confrmed the improved performance of the software fault prediction process.…”

Section: Applications Of Gwo Woa Hho and Mfo In Softwarementioning

confidence: 99%

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

et al. 2023

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Swarm intelligence algorithms are metaheuristics inspired by the collective behavior of species such as birds, fish, bees, and ants. They are used in many optimization problems due to their simplicity, flexibility, and scalability. These algorithms get the desired convergence during the search by balancing the exploration and exploitation processes. These metaheuristics have applications in various domains such as global optimization, bioinformatics, power engineering, networking, machine learning, image processing, and environmental applications. This paper presents a systematic literature review (SLR) on applications of four swarm intelligence algorithms i.e., grey wolf optimization (GWO), whale optimization algorithms (WOA), Harris hawks optimizer (HHO), and moth-flame optimizer (MFO) in the field of software engineering. It presents an in-depth study of these metaheuristics’ adoption in the field of software engineering. This SLR is mainly comprised of three phases such as planning, conducting, and reporting. This study covers all related studies published from 2014 up to 2022. The study shows that applications of the selected metaheuristics have been utilized in various fields of software engineering especially software testing, software defect prediction, and software reliability. The study also points out some of the areas where applications of these swarm intelligence algorithms can be utilized. This study may act as a guideline for researchers in improving the current state-of-the-art on generally adopting these metaheuristics in software engineering.

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Software fault prediction using Whale algorithm with genetics algorithm

Cited by 14 publications

References 50 publications

Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training

Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training

A survey on several new popular swarm intelligence optimization algorithms

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

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