Ekbal A. Rashid; Srikanta B. Patnaik; Vandana C. Bhattacherjee

Machine Learning and Software Quality Prediction: As an Expert System

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Author(s)

Ekbal A. Rashid ^1,* Srikanta B. Patnaik ² Vandana C. Bhattacherjee ³

1. Department of CS & E, C.I.T, Tatisilwai, Ranchi, 835103, India

2. Department of CS & E, SOA University,Bhubaneshwar, Orissa,751030, India

3. Department of CS & E, B.I.T, Mesra, 834001, Ranchi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2014.02.02

Received: 15 Jan. 2014 / Revised: 10 Feb. 2014 / Accepted: 11 Mar. 2014 / Published: 8 Apr. 2014

Index Terms

Machine Learning, Software Quality prediction, Case-Based Reasoning, Knowledge base building, Distance functions, Expert System

Abstract

To improve the software quality the number of errors from the software must be removed. The research paper presents a study towards machine learning and software quality prediction as an expert system. The purpose of this paper is to apply the machine learning approaches, such as case-based reasoning, to predict software quality. The main objective of this research is to minimize software costs. Predict the error in software module correctly and use the results in future estimation. The novel idea behind this system is that Knowledge base (KBS) building is an important task in CBR and the knowledge base can be built based on world new problems along with world new solutions. Second, reducing the maintenance cost by removing the duplicate record set from the KBS. Third, error prediction with the help of similarity functions. In this research four similarity functions have been used and these are Euclidean, Manhattan, Canberra, and Exponential. We feel that case-based models are particularly useful when it is difficult to define actual rules about a problem domain. For this purpose we have developed a case-based reasoning model and have validated it upon student data. It was observed that, Euclidean and Exponential both are good for error calculation in comparison to Manhattan and Canberra after performing five experiments. In order to obtain a result we have used indigenous tool. For finding the mean and standard deviation, SPSS version 16 and for generating graphs MATLAB 7.10.0 version have been used as an analyzing tool.

Cite This Paper

Ekbal A. Rashid, Srikanta B. Patnaik, Vandana C. Bhattacherjee, "Machine Learning and Software Quality Prediction: As an Expert System", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.6, no.2, pp.9-27, 2014. DOI:10.5815/ijieeb.2014.02.02

Reference

[1]G. Kadoda, M Cartwright, L Chen, and M. Shepperd. (2000), “Experiences Using Case- Based Reasoning to Predict Software Project Effort”, In Proceeding of EASE, p. 23-28, Keele, UK.

[2]I. Myrtveit and E. Stensrud. (1999), “A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models”, IEEE transactions on software Engineering, Vol 25, no. 4, pp. 510-525.

[3]K. Ganeasn, T.M. Khoshgoftaar, and E. Allen. (2000), “Case-based Software Quality Prediction”, International journal of Software Engineering and Knowledge Engineering, 10 (2), pp. 139-152.

[4]Bob Hughes & Mike Cotterell “software Project Management”, Tata McGraw-Hill.

[5]Shi Zhong,Taghi M.Khoshgoftaar and Naeem Selvia “Unsupervised Learning for Expert-Based Software Quality Estimation”.Proceeding of the Eighth IEEE International Symposium on High Assurance Systems Engineering (HASE’04).

[6]Ekbal Rashid, Srikanta Patnaik, Vandana Bhattacherjee “A Survey in the Area of Machine Learning and Its Application for Software Quality Prediction” has been published in ACM SigSoft ISSN 0163-5948, volume 37, number 5, September 2012, http://doi.acm.org/10.1145/2347696.2347709 New York, NY, USA.

[7]M. J. Khan, S. Shamail, M. M Awais, and T. Hussain, “Comparative study of various artificial intelligence techniques to predict software quality” in proceedings of the 10th IEEE multitopic conference, 2006, INMIC 06, PP 173-177, Dec 2006.

[8]S. Becker, L. Grunske, R. Mirandola, and S. Overhage, “ Performance prediction of component-based systems a survey from an engineering perspective”, In architechture systems with Trust-worthy components, Vol 3938 of LNCS, Springer, 2006.

[9]Ekbal Rashid, Srikanta Patnaik, Vandana Bhattacherjee “Enhancing the accuracy of case-based estimation model through Early Prediction of Error Patterns” proceedings published by the IEEE Computer Society 10662 Los Vaqueros Circle Los Alamitos, CA, in International Symposium on Computational and Business Intelligence (ISCBI 2013), New Delhi, 24~26 Aug 2013 ISBN 978-07695-5066-4/13 IEEE, DOI 10.1109/ISCBI.2013.

[10]Aamodt, A. and E. Plaza, Case-based reasoning: foundational issues, methodical variations and system approaches. AI Communications 7(1), 1994.

[11]M. M.T. Thwin and T.S. Quah, “Application of neural network for predicting software development faults using object-oriented design metrics” in proceeding of the 9th International Conference on neural information processing, ICONIP 02 Vol. 5, 2002.

[12]D. Grosser, H. A. Sahraoui, and P. Valtchev, “Analogy-based software quality prediction”, in 7th Workshop on Quantitative Approaches in Object-Oriented Software Engineering, QAOOSE 03, June 2003.

[13]T.W. Lioa, and Z. Zhang, “Similarity measures for retrieval in Case-Based Reasoning Systems” Applied Artificial Intelligence, Vol. 12, 1998, 267-288.

[14]Venkata U.B.Challagulla et al ”A Unified Framework for Defect data analysis using the MBR technique”. Proceeding of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’06).

[15]Lance, G. N.; Williams, W. T. (1966). "Computer programs for hierarchical polythetic classification ("similarity analysis")."Computer Journal 9 (1): 60–64. doi:10.1093/comjnl/9.1.60.

[16]Lance, G. N.; Williams, W. T. (1967). "Mixed-data classificatory programs I.) Agglomerative Systems". Australian Computer Journal: 15–20.

[17]Stephen H. Kan Metrics and Models in Software Quality Engineering, second edition by Pearson.

[18]Donald A. Waterman A guide to Expert Systems, First Impression, 2008, Pearson.

[19]Du Zhang, Jeffrey J. P. Tsai “Advances in Machine Learning Applications in Software Engineering” Idea Group Publishing.

[20]L. C. Briand, W. L.Melo and J. Wust. “Assessing the applicability of fault-proneness models across object-oriented software projects” IEEE Transactionson Software Engineering, 28(7): 706-720, July 2002.

[21]T. M. Khoshgoftaar and N. Seliya. Analogy-based practical classification rules for software quality estimation Empirical Software Engineering Journal, 8(4):325-350, December 2003.

[22]Begum, S. Ahmed, M.U.; Funk, P.; Ning Xiong; “Folke, M.Sch. of Innovation, Design & Eng., Malardalen Univ., Vasteras, Sweden, Published in Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on (Volume:41, Issue: 4), July 2011, ISSN :1094-6977, 10.1109/TSMCC.2010.2071862.

[23]“A Survey of measurement-based software quality prediction techniques” Technical Report, Lums, Dec 2007.

International Journal of Information Engineering and Electronic Business (IJIEEB)