scholarly journals Best Suited Machine Learning Techniques for Software Fault Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 4048-4053
Keyword(s):  
Machine Learning ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Module ◽  
Software Fault Prediction ◽  
Learning Techniques ◽  
Software Engineers ◽  
Software Fault ◽  
Prediction Techniques ◽  
A Company

In this world of emerging applications of software, it is always important to provide a quality assured product to customers. Software Fault Prediction popularly abbreviated as SFP is a major field which helps to provide quality assured products to customers. It helps to recognize modules that are bugfree and bug-prone in a software module. Machine learning techniques for both classification and determination are used for the purpose of software fault prediction. Software Fault Prediction is carried out prior to testing process without executing the source code, instead vital characteristics of software is taken into consideration. This early identification of faults can help software engineers to reduce the risk of system failure. A company does not always prefer to invest more expense on testing and in those situations, software fault prediction can have an upper hand in testing. The software fault prediction model will first train the learning techniques to generate base learners and then apply these base learners to unseen projects. It is always preferred to determine the count of faults rather than classifying each software module as fault-free and fault-prone. All software fault prediction techniques depend on base learners used and also nature of fault dataset. In this paper, the major learning techniques to determine software fault, characteristics of software fault dataset, etc. are discussed.

A Software Fault Prediction on Inter and Intra Release Prediction Scenarios

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Fault Prediction ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Software Modules ◽  
Software Fault

Software quality engineering applied numerous techniques for assuring the quality of software, namely testing, verification, validation, fault tolerance, and fault prediction of the software. The machine learning techniques facilitate the identification of software modules as faulty or non-faulty. In most of the research, these approaches predict the fault-prone module in the same release of the software. Although, the model is found to be more efficient and validated when training and tested data are taken from previous and subsequent releases of the software respectively. The contribution of this paper is to predict the faults in two scenarios i.e. inter and intra release prediction. The comparison of both intra and inter-release fault prediction by computing various performance matrices using machine learning methods shows that intra-release prediction is having better accuracy compared to inter-releases prediction across all the releases. Also, but both the scenarios achieve good results in comparison to existing research work.

Overview of Machine Learning Approaches

2017 ◽  
pp. 19-33
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Case Based Reasoning ◽  
Learning Approaches ◽  
Software Module ◽  
Learning Techniques ◽  
Case Based

This chapter enlists and presents an overview of various machine learning approaches. It also explains the machine learning techniques used in the area of software engineering domain especially case-based reasoning method. Case-based reasoning is used to predict software quality of the system by examining a software module and predicting whether it is faulty or non-faulty. In this chapter an attempt has been made to propose a model with the help of previous data which is used for prediction. In this chapter, how machine learning technique such as case-based reasoning has been used for error estimation or fault prediction. Apart from case-based reasoning, some other types of learning methods have been discussed in detail.

Important Issues in Software Fault Prediction

2014 ◽  
pp. 510-539 ◽  
Author(s):  
Golnoush Abaei ◽  
Ali Selamat
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Prediction Models ◽  
Research Field ◽  
Verification And Validation ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Fault Prediction ◽  
Learning Techniques ◽  
Software Fault

Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.

Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

2021 ◽  
pp. 693-709
Author(s):  
Wasiur Rhmann ◽  
Gufran Ahmad Ansari
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Software Testing ◽  
Prediction Models ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Software Fault Prediction ◽  
Ensemble Models ◽  
Software Fault

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.

Important Issues in Software Fault Prediction

2017 ◽  
pp. 162-190
Author(s):  
Golnoush Abaei ◽  
Ali Selamat
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Prediction Models ◽  
Research Field ◽  
Verification And Validation ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Fault Prediction ◽  
Learning Techniques ◽  
Software Fault

Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.

Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

2020 ◽  
Vol 11 (2) ◽  
pp. 33-48
Author(s):  
Wasiur Rhmann ◽  
Gufran Ahmad Ansari
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Software Testing ◽  
Prediction Models ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Fault Prediction ◽  
Ensemble Models ◽  
Ensemble Techniques ◽  
Software Fault

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.

Machine learning based methods for software fault prediction: A survey

2021 ◽  
Vol 172 ◽  
pp. 114595
Author(s):  
Sushant Kumar Pandey ◽  
Ravi Bhushan Mishra ◽  
Anil Kumar Tripathi
Keyword(s):  
Machine Learning ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Software Fault
Sign in / Sign up

Export Citation Format

Share Document