CFP -- Special issue: Predictive Analytics in Software Engineering
Software Quality Journal
Software systems are increasingly large and complex, making activities
related to ensuring software resource allocation and decision making under
uncertainty increasingly difficult. In this context, techniques able to
automatically retrieve knowledge from software data in order to improve
decision-making are highly desirable. Predictive analytics has shown
promising results in this area. For instance, predictive analytics can be
used to uncover the relationship between features retrieved from software
processes, software usage or software itself as well as to discover certain
properties of interest, such as the presence of bugs, the likelihood of
changes leading to crashes, the presence of code smells, dependencies, etc.
Such knowledge can be particularly useful to enhance decision-making
process in managing large and complex systems, potentially contributing to
improve software quality.
With this in mind, this special issue aims at investigating predictive
analytics in software engineering. We would also like to encourage
submissions that provide an in depth understanding of when, why and how
algorithms to create predictive models work. We believe that such
understanding will greatly benefit the software engineering community,
given that it will improve the external validity of studies and provide
insights into how to improve algorithms further.
The topics of this special issue include but are not limited to:
- Predicting or detecting defects / faults / bugs, crash-prone and
bug-prone commits, and code smells.
- Predictive models in search-based software engineering.
- Predictive models for dealing with multiple objectives in software
- Predictive models for policy and decision-making that affects software
development and quality.
- Predictive models for software engineering in different settings, e.g.
lean/agile, waterfall, distributed, community-based software development.
- Empirical studies involving predictive modelling in software development
- Industrial experience reports and insights on predictive modelling in
software development and quality.
- The effectiveness of human experts vs. automated models in building
- Verifying / refuting / challenging previous theory and results on
predictive models in software development and quality.
- Building recommender systems in software engineering.
- Predictive analytics algorithms (such as Bayesian Networks, Markov,
ensemble methods, etc.) and their application to software engineering.
Prof. Ayse Bener, Ryerson University
Dr. Leandro L. Minku, University of Leicester
Prof. Burak Turhan, University of Oulu
Authors are encouraged to submit high-quality, original work that has
neither appeared in, nor is under consideration by, other journals.
Submissions extending previous work published at other venues must include
at least 30% new material. All papers must be submitted online using the
Editorial Manager. Our online system offers authors the ability to track
the review process of their manuscript. This online system offers easy and
straightforward log-in and submission procedures, and supports a wide range
of submission file formats. Manuscript should be submitted to:
http://SQJO.edmgr.com. Choose “S.I.: Predictive Analytics” as the article
March 15, 2016
Each paper will be reviewed by at least 2 reviewers and judged based on:
- Presentation and clarity
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