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 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 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

scholarly journals Predicting Bank Failures with Machine Learning Algorithms: A Comparison of Boosting and Cost-Sensitive Models

2021 ◽  
Vol 3 (2) ◽  
pp. 43-50
Author(s):  
Safa SEN ◽  
Sara Almeida de Figueiredo
Keyword(s):  
Machine Learning ◽  
Early Warning Systems ◽  
Machine Learning Algorithms ◽  
Bank Failures ◽  
Warning Systems ◽  
Economic Prosperity ◽  
Software Defect ◽  
Turbulent Times ◽  
The Cost ◽  
Prediction Capability

Predicting bank failures has been an essential subject in literature due to the significance of the banks for the economic prosperity of a country. Acting as an intermediary player of the economy, banks channel funds between creditors and debtors. In that matter, banks are considered the backbone of the economies; hence, it is important to create early warning systems that identify insolvent banks from solvent ones. Thus, Insolvent banks can apply for assistance and avoid bankruptcy in financially turbulent times. In this paper, we will focus on two different machine learning disciplines: Boosting and Cost-Sensitive methods to predict bank failures. Boosting methods are widely used in the literature due to their better prediction capability. However, Cost-Sensitive Forest is relatively new to the literature and originally invented to solve imbalance problems in software defect detection. Our results show that comparing to the boosting methods, Cost-Sensitive Forest particularly classifies failed banks more accurately. Thus, we suggest using the Cost-Sensitive Forest when predicting bank failures with imbalanced datasets.

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.

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