Software Quality Estimation using Machine Learning: Case-based Reasoning Technique
TL;DR: This paper advocates the use of case-based reasoning (i.e., CBR) to make a software quality estimation system by the help of human experts by using different similarity measures to find the best method which increases estimation accuracy & reliability.
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Abstract: Software quality estimation is one of the most interesting research areas in the domain of software engineering for last few decades. Large numbers of techniques and models have already been worked out in the area of error estimation. The aim of software quality estimation is to identify error prone tasks as the cost can be minimized with advance knowledge about the errors and this early treatment of error will enhance the software quality. In this paper we have explored a set of data in university setting. This paper advocates the use of case-based reasoning (i.e., CBR) to make a software quality estimation system by the help of human experts. CBR relies on historical information from similar past projects, whereby similarities are determined by comparing the projects, and key attributes. We have used different similarity measures to find the best method which increases estimation accuracy & reliability. This paper presents a work in which we have expanded our previous work [24]. The software is a console based application and thus does not use the GUI functions of the Operating System, which makes it very fast in execution. In order to obtain results we have used an indigenous tool for software quality estimation, run in c++ compiler. General Terms Software Engineering: Quality
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Citations
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
A Literature Review Study of Software Defect Prediction using Machine Learning Techniques
Feidu Akmel,Ermiyas Birihanu,Bahir Siraj +2 more
- 29 Jun 2018
TL;DR: In this study, the application of machine learning on software defect was discovered by using data gathered from source code through software metrics, which can be an input for software defect predictor.
18
•Proceedings Article
Software project management
Didkovska,Machulyanskiy +1 more
- 01 Jan 2011
TL;DR: In this paper, the structural analysis of technology project management is carried out and relationships between the processes of management are established, and formalization is accomplished and the mathematical model of software project management are proposed.
15
Analyzing the non-functional requirements to improve accuracy of software effort estimation through case based reasoning
Fadoua Fellir,Khalid Nafil,Rajaa Touahni +2 more
- 17 Dec 2015
TL;DR: An early software size and effort estimation method based on a combination of COSMIC and case based reasoning that uses individual requirement measurements as a solution to improve the performance of CBR and to increase the precision of the estimations is proposed.
7
References
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Effort estimation using analogy
Martin Shepperd,Chris Schofield,Barbara Kitchenham +2 more
- 01 May 1996
TL;DR: An automated environment known as ANGEL is described that supports the collection, storage and identification of the most analogous projects in order to estimate the effort for a new project and is shown to out perform traditional algorithmic methods for six different datasets.
•Book
Software Project Management
Mike Cotterell,Bob Hughes +1 more
- 01 Jul 1995
TL;DR: Software project management is a crucial element in successful software and IT development, and requires students to develop an understanding of technical methodology and an appreciation of the many human factors that can play a part in software projects as mentioned in this paper.
A controlled experiment to assess the benefits of estimating with analogy and regression models
Ingunn Myrtveit,Erik Stensrud +1 more
TL;DR: An experiment to replicate previous studies which claim that estimation by analogy outperforms regression models is described, finding that the results do not converge with previous results.
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