NDLI: Defect Prediction between Software Versions with Active Learning and Dimensionality Reduction

Content Provider	IEEE Xplore Digital Library
Author	Huihua Lu Kocaguneli, E. Cukic, B.
Copyright Year	2014
Description	Author affiliation: Lane Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV, USA (Huihua Lu; Kocaguneli, E.; Cukic, B.)
Abstract	Accurate detection of defects prior to product release helps software engineers focus verification activities on defect prone modules, thus improving the effectiveness of software development. A common scenario is to use the defects from prior releases to build the prediction model for the upcoming release, typically through a supervised learning method. As software development is a dynamic process, fault characteristics in subsequent releases may vary. Therefore, supplementing the defect information from prior releases with limited information about the defects from the current release detected early seems to offer intuitive and practical benefits. We propose active learning as a way to automate the development of models which improve the performance of defect prediction between successive releases. Our results show that the integration of active learning with uncertainty sampling consistently outperforms the corresponding supervised learning approach. We further improve the prediction performance with feature compression techniques, where feature selection or dimensionality reduction is applied to defect data prior to active learning. We observe that dimensionality reduction techniques, particularly multidimensional scaling with random forest similarity, work better than feature selection due to their ability to identify and combine essential information in data set features. We present the improvements offered by this methodology through the prediction of defective modules in the three successive versions of Eclipse.
Starting Page	312
Ending Page	322
File Size	388399
Page Count	11
File Format	PDF
ISSN	10719458
e-ISBN	9781479960330
DOI	10.1109/ISSRE.2014.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-03
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Software Uncertainty Predictive models Radio frequency Measurement Supervised learning Computational modeling Machine learning Software defect prediction Complexity measures Active learning Dimensionality reduction
Content Type	Text
Resource Type	Article

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