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Investigating the Effect of Software Metrics Aggregation on Software Fault Prediction

Topics: Automated Software Engineering; Data-driven Software Engineering; Empirical Software Engineering; Quality Management; Software Development Lifecycle; Software Engineering Tools; Software Project Planning and Tracking; Testing and Testability

Authors: Deepanshu Dixit 1 and Sandeep Kumar 2

Affiliations: 1 Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee and India ; 2

ISBN: 978-989-758-320-9

Keyword(s): Software Fault Prediction, Aggregation of Software Metrics, Average Absolute Deviation, Interquartile Range.

Abstract: In inter-releases software fault prediction, the data from the previous version of the software that is used for training the classifier might not always be of same granularity as that of the testing data. The same scenario may also happen in the cross project software fault prediction. So, one major issue in it can be the difference in granularity i.e. training and testing datasets may not have the metrics at the same level. Thus, there is a need to bring the metrics at the same level. In this paper, aggregation using Average Absolute Deviation (AAD) and Interquartile Range (IQR) are explored. We propose the method for aggregation of metrics from class to package level for software fault prediction and validated the approach by performing experimental analysis. We did the experimental study to analyze the performance of software fault prediction mechanism when no aggregation technique was used and when the two mentioned aggregation techniques were used. The experimental study reveale d that the aggregation improved the performance and out of AAD and IQR aggregation techniques, IQR performs relatively better. (More)

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Paper citation in several formats:
Dixit, D. and Kumar, S. (2018). Investigating the Effect of Software Metrics Aggregation on Software Fault Prediction.In Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-320-9, pages 304-311. DOI: 10.5220/0006884003380345

@conference{icsoft18,
author={Deepanshu Dixit. and Sandeep Kumar.},
title={Investigating the Effect of Software Metrics Aggregation on Software Fault Prediction},
booktitle={Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2018},
pages={304-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006884003380345},
isbn={978-989-758-320-9},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Investigating the Effect of Software Metrics Aggregation on Software Fault Prediction
SN - 978-989-758-320-9
AU - Dixit, D.
AU - Kumar, S.
PY - 2018
SP - 304
EP - 311
DO - 10.5220/0006884003380345

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