Journal of Software: Evolution and Process

Cover image for Vol. 26 Issue 12

Early View (Online Version of Record published before inclusion in an issue)

Edited By: Gerardo Canfora, Darren Dalcher and David Raffo

Impact Factor: 1.32

ISI Journal Citation Reports © Ranking: 2013: 32/105 (Computer Science Software Engineering)

Online ISSN: 2047-7481

Associated Title(s): Concurrency and Computation: Practice and Experience, Software Focus, Software Process: Improvement and Practice, Software Testing, Verification and Reliability, Software: Practice and Experience

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  1. Special Issue Papers

    1. Big data clone detection using classical detectors: an exploratory study

      Jeffrey Svajlenko, Iman Keivanloo and Chanchal K. Roy

      Article first published online: 29 SEP 2014 | DOI: 10.1002/smr.1662

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      Big data clone detection across tens of thousands of software systems has several applications, including API usage recommendation, code completion, and search driven development. However, the state-of-the-art tools are designed to scale to only single software systems. We develop a scalability heuristic that scales these classical tools to tens of thousands of software systems using commodity hardware and evaluate its performance experimentally.

    2. On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation

      S. Amasaki and C. Lokan

      Article first published online: 20 AUG 2014 | DOI: 10.1002/smr.1672

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      On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation Authors: S. Amasaki and C. Lokan mini-abst(80words): It seems effective to use a window of training data so that an effort estimation model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. We examined the effects of weighted moving windows on effort estimation accuracy. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, though the methods also significantly worsened accuracy in smaller windows.

    3. Do feelings matter? On the correlation of affects and the self-assessed productivity in software engineering

      Daniel Graziotin, Xiaofeng Wang and Pekka Abrahamsson

      Article first published online: 6 AUG 2014 | DOI: 10.1002/smr.1673

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      This article provides basic theoretical building blocks on researching the human side of software development in empirical software engineering with psychological measurements. It examines the correlation of the affects and the performance of software developers working in natural settings on real-world software projects. The results show that the real-time affects related to a software development task are positively correlated with a programmer's self-assessed productivity. This article highlights the body of knowledge in psychology and management research on the affects and their impact on performance.

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