scholarly journals AN OPTIMIZED MUTATION TESTING USING HYBRID METAHEURISTIC TECHNIQUE WITH MACHINE LEARNING FOR SOFTWARE DEFECT PREDICTION

2021 ◽  
Vol 10 (3) ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Prediction Model ◽  
Test Data ◽  
Mutation Testing ◽  
Defect Prediction ◽  
Hybrid Technique ◽  
Software Defect Prediction ◽  
Hybrid Metaheuristic ◽  
Software Defect

Software defect prediction model based on the mutation testing is a pioneering method for the fault-based unit testing in which faults are detected by executing certain test data. This paper presents an Optimized Mutation Testing (OMT) technique based software defect prediction model using the concept of hybrid metaheuristic technique. Here, hybridization of OMT with Enhanced Learning-to-Rank (ELTR) is used for the feature extraction from mutation testing based data generation mechanism. In the proposed approach, first hybrid technique is used for the test data feature extraction then this data is exercised to cover all mutants present in the specific program under test and then machine learning based Random Forest as an ensemble classifier is used as a classifier. The proposed method can improve the testing as well defect prediction efficiency by deleting the redundant test data. In this research work, two models are implemented for the software defect prediction using the ELTR and LTR. At last, the performance parameters such as Detection Rate, Defect Predication Value, Execution Time, Percentage of Fault Negative Rate and Percentage of Fault Rate are measured and compared with the existing work to validate the proposed model.

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.

Research of Software Defect Prediction Model Based on ACO-SVM

2011 ◽  
Vol 34 (6) ◽  
pp. 1148-1154 ◽  
Author(s):  
Hui-Yan JIANG ◽  
Mao ZONG ◽  
Xiang-Ying LIU
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Model Based ◽  
Software Defect

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

A Novel Effort Measure Method for Effort-Aware Just-in-Time Software Defect Prediction

2021 ◽  
Vol 31 (08) ◽  
pp. 1145-1169
Author(s):  
Liqiong Chen ◽  
Shilong Song ◽  
Can Wang
Keyword(s):  
Prediction Model ◽  
Defect Prediction ◽  
Software Systems ◽  
Support Vector ◽  
Just In Time ◽  
Software Defect Prediction ◽  
Fine Grained ◽  
Software Defect ◽  
Code Changes ◽  
The Cost

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.

Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 53-75 ◽  
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rapid Development ◽  
Defect Prediction ◽  
Instance Selection ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Radial Basis ◽  
Base Balance

Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach.

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