A PSO-GA approach targeting fault-prone software modules

Moussa, Rebecca; Azar, Danielle

doi:10.1016/j.jss.2017.06.059

Cited by 20 publications

(6 citation statements)

References 14 publications

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“…This includes widely used Machine Learning techniques such as Decision Trees, Logistic Regression and Naive Bayes [16,27,36]. More advanced techniques such as ensemble learning and search-based approaches have also been successfully applied for predicting software defects [3,15,20,29,43,48,57,61]. However, no previous study has investigated the application of search-based approaches for the generation of ensemble for defect prediction.…”

Section: Defect Predictionmentioning

confidence: 99%

“…Search-based Software Engineering has been shown to be a powerful tool to address Software Engineering prediction tasks [60], such as software effort estimation, change prediction, defect prediction and maintainability prediction [41]. In the context of defect prediction previous studies have investigated the use of both single-(e.g., [30,48,62,68]) and multi-objective search-based approaches [15] to either build or fine-tune learning models. However, no previous defect prediction study has investigated the use of search-based approaches to guide the construction of ensemble models, which have instead been exploited to solve general-purpose classification tasks (see Section 6.2).…”

Section: Related Workmentioning

confidence: 99%

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MEG: Multi-objective Ensemble Generation for Software Defect Prediction

Moussa

Guizzo

Sarro

2022

Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement

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Background: Defect Prediction research aims at assisting software engineers in the early identification of software defect during the development process. A variety of automated approaches, ranging from traditional classification models to more sophisticated learning approaches, have been explored to this end. Among these, recent studies have proposed the use of ensemble prediction models (i.e., aggregation of multiple base classifiers) to build more robust defect prediction models. Aims: In this paper, we introduce a novel approach based on multi-objective evolutionary search to automatically generate defect prediction ensembles. Our proposal is not only novel with respect to the more general area of evolutionary generation of ensembles, but it also advances the state-of-the-art in the use of ensemble in defect prediction. Method: We assess the effectiveness of our approach, dubbed as Multi-objective Ensemble Generation (MEG), by empirically benchmarking it with respect to the most related proposals we found in the literature on defect prediction ensembles and on multi-objective evolutionary ensembles (which, to the best of our knowledge, had never been previously applied to tackle defect prediction). Result: Our results show that MEG is able to generate ensembles which produce similar or more accurate predictions than those achieved by all the other approaches considered in 73% of the cases (with favourable large effect sizes in 80% of them). Conclusions: MEG is not only able to generate ensembles that yield more accurate defect predictions with respect to the benchmarks considered, but it also does it automatically, thus relieving the engineers from the burden of manual design and experimentation. CCS CONCEPTS• Software and its engineering → Search-based software engineering; Software defect analysis; • Computing methodologies → Ensemble methods.

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Section: Defect Predictionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MEG: Multi-objective Ensemble Generation for Software Defect Prediction

Moussa

Guizzo

Sarro

2022

Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement

Self Cite

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“…From observations the introduced method emerged as a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network. Yu et al used a hybrid PSO-GA to estimate energy demand of China in [57] whereas Moussa and Azar introduced a hybrid algorithm to classify software modules as fault-prone or not using object-oriented metrics in [58]. Nik et al used GA-PSO, PSO-GA and a collection of other hybridization approaches to optimize surveyed asphalt pavement inspection units in massive networks [59].…”

Section: Hybridization Of Pso Using Genetic Algorithms (Ga)mentioning

confidence: 99%

“…Benvidi et al [56] 2016 GA-PSO Spectrophotometric determination of synthetic colorants Yu et al [57] 2011 GA-PSO Estimation of Energy Demand Moussa and Azar [58] 2017 PSO-GA Classification Nik, Nejad and Zakeri [59] 2016 GA-PSO, PSO-GA Optimization of Surveyed Asphalt Pavement Inspection Unit Garg [60] 2015 GA-PSO Constrained Optimization…”

Section: Author/smentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

394

184

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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“…Moussa, and D. Azar [12] presented Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) algorithm for classify the fault-prone software modules. The search direction in a particular region of search space is guided by the PSO then GA constructs a classifier recombination.…”

Section: Literature Reviewmentioning

confidence: 99%

Software Fault Prediction and Classification using Cost based Random Forest in Spiral Life Cycle Model

Premalatha¹,

Srikrishna²

2018

IJIES

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Abstract:In the domain of software engineering many new techniques are deployed for identifying the fault in software modules. This part of software design plays a fundamental role cause of its assurance towards higher reliability and stability. Many existing techniques like Bayesian approach have been employed to minimize the software faults but they can't able to predict efficiently within limited resources. In this paper, a new classification and prediction methodology is put forth to progress the accuracy of defect forecast based on Cost Random Forest algorithm (CRF) which reduces the effects of faults in irrelevant software modules. The proposed algorithm predicts the quantity of faults present in the modules of software in less time and classify based on measures of similarity obtained from Robust Similarity clustering technique. The overall results inferred from this methodology proven that this CRF can be capable to rank the module's faults in order to enhance the software development quality.

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A PSO-GA approach targeting fault-prone software modules

Cited by 20 publications

References 14 publications

MEG: Multi-objective Ensemble Generation for Software Defect Prediction

MEG: Multi-objective Ensemble Generation for Software Defect Prediction

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Software Fault Prediction and Classification using Cost based Random Forest in Spiral Life Cycle Model

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