Investigating Implications of Metric Based Predictive Data Mining Approaches towards Software Fault Predictions

2018 ◽  
Vol 7 (3.12) ◽  
pp. 427
Author(s):  
Pooja Kapoor ◽  
Deepak Arora ◽  
Ashwani Kumar
Keyword(s):  
Research Work ◽  
Research Field ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Critical Examination ◽  
Data Set ◽  
Software Fault Prediction ◽  
Software Fault ◽  
Predictive Data Mining

Context: Since 1990, various researches have been working in the area of software fault prediction but yet it is difficult to assess the impacts and progressive path of this research field. Objective: In this research work, author’s major objective is to investigate the context and dimensions of research studies performed by different researchers in the area of software fault prediction. This work also focuses on presenting a well defined systematic view of their findings and suggestions after a critical examination of all major approaches applied in this key research area. Method: This research work includes 112 total manuscripts published between 2009 and 2014. These studies are gathered from a pool of total 587 manuscripts. The selection criteria for these manuscripts are title, keywords and citation of that paper. Result: The results of this investigation shows that most of the research work related to software fault prediction have been performed on available data set from NASA repository. Most of the research work performed is basically confined to analysis or comparative study of various machine learning techniques based on their classification accuracy. Various research work published doesn’t exhibit clearer representation of any specific prediction model. Conclusion: Still after years of development, there is a huge gap between the industry requirement and the research being performed by different researchers in the field of Software fault prediction. A better collaboration between industry academia is still required. This research work represents a critical investigative approach towards finding the exact gaps to be filled and explored more authentic future research areas in this field. All result finding have been critically examined and compared with existing literature work for better understanding and deep insight over identifying the major strengths of chosen research field. 

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.

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.

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.

Performance Comparison of Various Algorithms During Software Fault Prediction

2021 ◽  
Vol 13 (2) ◽  
pp. 70-94
Author(s):  
Munish Khanna ◽  
Abhishek Toofani ◽  
Siddharth Bansal ◽  
Mohammad Asif
Keyword(s):  
Roc Analysis ◽  
Performance Comparison ◽  
Fault Prediction ◽  
Data Set ◽  
Software Fault Prediction ◽  
The Public ◽  
Machine Learning Model ◽  
Software Fault ◽  
Early Phases ◽  
Volume Size

Producing software of high quality is challenging in view of the large volume, size, and complexity of the developed software. Checking the software for faults in the early phases helps to bring down testing resources. This empirical study explores the performance of different machine learning model, fuzzy logic algorithms against the problem of predicting software fault proneness. The work experiments on the public domain KC1 NASA data set. Performance of different methods of fault prediction is evaluated using parameters such as receiver characteristics (ROC) analysis and RMS (root mean squared), etc. Comparison is made among different algorithms/models using such results which are presented in this paper.

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.

Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective

2014 ◽  
Vol 24 (01) ◽  
pp. 61-90 ◽  
Author(s):  
Baojun Ma ◽  
Huaping Zhang ◽  
Guoqing Chen ◽  
Yanping Zhao ◽  
Bart Baesens
Keyword(s):  
Prediction Models ◽  
Prediction Performance ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
Associative Classification ◽  
Production Environment ◽  
Software Fault Prediction ◽  
Software Fault ◽  
Real World Datasets

It is a recurrent finding that software development is often troubled by considerable delays as well as budget overruns and several solutions have been proposed in answer to this observation, software fault prediction being a prime example. Drawing upon machine learning techniques, software fault prediction tries to identify upfront software modules that are most likely to contain faults, thereby streamlining testing efforts and improving overall software quality. When deploying fault prediction models in a production environment, both prediction performance and model comprehensibility are typically taken into consideration, although the latter is commonly overlooked in the academic literature. Many classification methods have been suggested to conduct fault prediction; yet associative classification methods remain uninvestigated in this context. This paper proposes an associative classification (AC)-based fault prediction method, building upon the CBA2 algorithm. In an empirical comparison on 12 real-world datasets, the AC-based classifier is shown to achieve a predictive performance competitive to those of models induced by five other tree/rule-based classification techniques. In addition, our findings also highlight the comprehensibility of the AC-based models, while achieving similar prediction performance. Furthermore, the possibilities of cross project prediction are investigated, strengthening earlier findings on the feasibility of such approach when insufficient data on the target project is available.

Literature Survey and Scope of the Present Work

2017 ◽  
pp. 9-18
Keyword(s):  
Research Work ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Advantages And Disadvantages ◽  
Large Numbers ◽  
Learning Techniques ◽  
Software Quality Prediction

As I know large numbers of techniques and models have already been worked out in the area of error estimation. Identifying and locating errors in software projects is a complicated job. Particularly, when project sizes grow. This chapter enlists and reviews existing work to predict the quality of the software using various machine learning techniques. In this chapter key finding from prior studies in the field of software fault prediction has been discussed. Various advantages and disadvantages of the methods used for software quality prediction, have been explained in a detail. What are the problems solved are also mentioned in this section. Description of earlier research work and present research work has summarized in one place.

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