scholarly journals Machine Learning Algorithms in Software Defect Prediction Analysis

2019 ◽  
Vol 8 (9) ◽  
pp. 2699-2702
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Misclassification Cost ◽  
Prediction Analysis ◽  
Software Defect

Programming deformity forecast assumes a vital job in keeping up great programming and decreasing the expense of programming improvement. It encourages venture directors to assign time and assets to desert inclined modules through early imperfection identification. Programming imperfection expectation is a paired characterization issue which arranges modules of programming into both of the 2 classifications: Defect– inclined and not-deformity inclined modules. Misclassifying imperfection inclined modules as not-deformity inclined modules prompts a higher misclassification cost than misclassifying not-imperfection inclined modules as deformity inclined ones. The machine learning calculation utilized in this paper is a blend of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplace Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The proposed Algorithm is assessed and indicates better execution and low misclassification cost when contrasted and the 3 algorithms executed independently.

scholarly journals Software Defect Prediction Analysis by using Machine Learning Algorithms

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3544-3546
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Misclassification Cost ◽  
Prediction Analysis ◽  
Software Defect ◽  
The Cost

Programming deformation gauge expect a crucial activity in keeping up extraordinary programming and diminishing the cost of programming improvement. It urges adventure executives to relegate time and advantages for desert slanted modules through early flaw distinguishing proof. Programming flaw desire is a matched portrayal issue which orchestrates modules of programming into both 2 arrangements: Defect– slanted and not-deformation slanted modules. Misclassifying blemish slanted modules as not-disfigurement slanted modules prompts a higher misclassification cost than misclassifying not-flaw slanted modules as deformation slanted ones. The AI estimation used in this paper is a mix of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive Laplace Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The proposed Algorithm is surveyed and demonstrates better execution and low misclassification cost when differentiated and the 3 calculations executed autonomously.

scholarly journals Software Defect Prediction Using Machine Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1053-1057
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Prediction Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Faults ◽  
Prediction Analysis ◽  
Software Defect

Software defect prediction analysis is an important problem in the software engineering community. Software defect prediction can directly affect the quality and has achieved significant popularity in the last few years. This software prediction analysis helps in delivering the best development and makes the maintenance of software more reliable. This is because predicting the software faults in the earlier phase improves the software quality,efficiency, reliability and the overall cost in SDLC. Developing and improving the software defect prediction model is a challenging task and many techniques are introducing for better performance. Supervised ML algorithms have been used to predict future software faults based on historical data[1]. These classifiers are Naïve Bayes (NB), Support Vector Machine(SVM) and Artificial neural network(ANN). The evaluation process showed that ML algorithms can be used effectively with a high accuracy rate. The comparison is made with other machine learning algorithms to finds the algorithms which gives more accuracy. And the results show that machine learning algorithms gives the best performance. The existence of software defects affects dramatically on software reliability, quality, and maintenance cost. Achieving reliable software also is hard work, even the software applied carefully because most time there is hidden errors. In addition, developing a software defect prediction model which could predict the faulty modules in the early phase is a real challenge in software engineering. Software defect prediction analysis is an essential activity in software development. This is because predicting the bugs prior to software deployment achieves user satisfaction, and helps in increasing the overall performance of the software. Moreover, predicting software defects early improves software adaptation to different environments and increases resource utilization.

SDP-ML: An Automated Approach of Software Defect Prediction employing Machine Learning Techniques

2021 ◽  
Author(s):  
Md Nasir Uddin ◽  
Bixin Li ◽  
Md Naim Mondol ◽  
Md Mostafizur Rahman ◽  
Md Suman Mia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Learning Techniques

Software defect prediction: A multi-criteria decision-making approach

2020 ◽  
Vol 15 (1) ◽  
pp. 35-42
Author(s):  
A.O. Balogun ◽  
A.O. Bajeh ◽  
H.A. Mojeed ◽  
A.G. Akintola
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Evaluation Metrics ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Defect Prediction ◽  
Learning Models ◽  
Decision Method ◽  
Software Defect ◽  
Machine Learning Models

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combination of machine learning models as a form of an ensemble is better than single SDP models in terms of prediction accuracy. However, the efficiency of machine learning models can change with diverse predictive evaluation metrics. Thus, more studies are needed to establish the effectiveness of ensemble SDP models over single SDP models. This study proposes the deployment of Multi-Criteria Decision Method (MCDM) techniques to rank machine learning models. Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) which are types of MCDM techniques are deployed on 9 machine learning models with 11 performance evaluation metrics and 11 software defects datasets. The experimental results showed that ensemble SDP models are best appropriate SDP models as Boosted SMO and Boosted PART ranked highest for each of the MCDM techniques. Besides, the experimental results also validated the stand of not considering accuracy as the only performance evaluation metrics for SDP models. Conclusively, more performance metrics other than predictive accuracy should be considered when ranking and evaluating machine learning models. Keywords: Ensemble; Multi-Criteria Decision Method; Software Defect Prediction

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