Proceedings Article10.1145/1882362.1882379
Analytics for software development
Raymond P.L. Buse,Thomas Zimmermann +1 more
- 07 Nov 2010
- pp 77-80
TL;DR: This paper proposes software analytics which holds out the promise of helping the managers of software projects turn their plentiful information resources, produced readily by current tools, into insights they can act on.
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Abstract: Despite large volumes of data and many types of metrics, software projects continue to be difficult to predict and risky to conduct. In this paper we propose software analytics which holds out the promise of helping the managers of software projects turn their plentiful information resources, produced readily by current tools, into insights they can act on. We discuss how analytics works, why it's a good fit for software engineering, and the research problems that must be overcome in order to realize its promise.
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Citations
A unified foundation for business analytics
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TL;DR: It is found that business analytics involves issues quite aside from data management, number crunching, technology use, systematic reasoning, and so forth, and a relatively comprehensive and holistic foundation of business analytics is introduced.
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TL;DR: Five distinct working styles of data scientists are identified: Insight Providers, who work with engineers to collect the data needed to inform decisions that managers make; Modeling Specialists, who use their machine learning expertise to build predictive models; Platform Builders, who create data platforms, balancing both engineering and data analysis concerns; and Team Leaders, who run teams of data Scientists and spread best practices.
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Andrew Begel,Thomas Zimmermann +1 more
- 31 May 2014
TL;DR: In this article, the authors present results from two surveys related to data science applied to software engineering, one survey solicited questions that software engineers would like data scientists to investigate about software, about software processes and practices, and about software engineers.
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TL;DR: The data and analysis needs of professional software engineers are presented, which were identified among 110 developers and managers in a survey, and several guidelines for analytics tools in software development are proposed.
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References
A Complexity Measure
TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.
6K
•Book
A complexity measure
Thomas J. McCabe
- 04 Oct 1993
TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.
5.1K
Building knowledge through families of experiments
TL;DR: The paper discusses the experience of the authors, based upon a collection of experiments, in terms of a framework for organizing sets of related studies, with specific emphasis on persistent problems encountered in experimental design, threats to validity, criteria for evaluation, and execution of experiments in the domain of software engineering.
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Thomas Zimmermann,Nachiappan Nagappan,Harald C. Gall,Emanuel Giger,Brendan Murphy +4 more
- 24 Aug 2009
TL;DR: This paper studied cross-project defect prediction models on a large scale and identified factors that do influence the success of cross- project predictions, and derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.
775
Information Needs in Collocated Software Development Teams
Andrew J. Ko,Robert DeLine,Gina Venolia +2 more
- 24 May 2007
TL;DR: This work analyzed software developers' day-to-day information needs at a large software company and transcribed their activities in go-minute sessions to identify information types and cataloged the outcome and source when each type of information was sought.
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