Effective Fault Prediction Model Developed Using Source Code Metrics

Improving quality of the desired software is an important topic under software engineering domain that has attracted several researchers’ attention. Prediction and early identification of fault-prone components help the maintenance team, as they can optimize and focus on their testing resources on the modules. Software quality prediction models are generally developed using a structural measurement of software (source code metrics (SCMs)). In this tutorial, we will present the relation between the internal structural properties with relevant external system qualities such as reliability, change-proneness, maintainability, etc.. We will introduce the basic use of various artificial intelligence (AI) techniques and feature selection (FS) methods for software fault prediction. The focus of our tutorial is on the effectiveness of fault prediction models. In particular, we will focus on three important concepts: (1) a framework to validate the source code metrics and identify a suitable set of source code metrics with the aim to reduce irrelevant features and improve the performance of the fault prediction model. (2) Development of fault prediction models different machine learning techniques and different ensemble methods. (3) A framework to evaluate the effectiveness of the developed fault prediction models. In addition to the basic introduction and motivation, we will discuss the open research problems, important literature, proposed approach, experimental results, and future directions.

Presenter Information

Dr. Lov Kumar is an Assistant Professor in Department of Computer Science and Information Systems at BITS Pilani-Hyderabad, India. His research interests are in the area of Quality estimation, web service, Object-Oriented programming and service oriented programming. He has a PhD in Computer Science (Predicting Software Quality Parameters using Artificial Intelligence Techniques and Source Code Metrics) from NIT Rourkerla. He was a Faculty Member (at Thapar University) from Aug 2017 to Dec 2017. He has published several research papers in international conferences and journals. He is a member of different professional bodies such as IEEE, ACM, CSI, IRD etc.

Dr. Sangeeta is working as an assistant professor in the department of computer science in Jayppe Institute of Information, Technology (JIIT), Noida, from April-2012. She has complete her PhD from the Department of Computer Science and Engineering (CSE) at JIIT. She received her M. Tech in Computer Science from Indraprastha Institute of Information Technology, Delhi (IIITD), India, in 2011; M.Sc in Computer Science from University of Delhi, India, in 2009; BSc(H) in computer science from University of Delhi, in 2007. She has published several papers in international and national journals and conferences. She is currently serving in Editorial Review Board (ERB) of IJOSSP Journal. She has served as a program committee member for India Software Engineering Conference (ISEC), 2016. She has served as a reviewer for several Journals and conferences: IJOS2017, ICIoTCT-2018, ICCIDS-2018, ISEC-2016, ISEC- 2015, SANER-2015, ICSE SEET-2014, APSEC-2014, APSEC-2013,BDA-2013, ISEC-2013. She has served as session chair in ICIoTCT-2018 conference.

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