Software systems have been playing important roles in business, scientific research, and our everyday lives. It is critical to improve both software productivity and quality, which are major challenges to software engineering researchers and practitioners. In recent years, software mining has emerged as a promising means to address these challenges. It has been successfully applied to discover knowledge from software artifacts (e.g., specifications, source code, documentations, execution logs, and bug reports) to improve software quality and development process (e.g., to obtain the insights for the causes leading to poor software quality, to help software engineers locate and identify problems quickly, and to help the managers optimize the resources for better productivity). Software mining has attracted much attention in both software engineering and data mining communities.
The Sixth International Workshop on Software Mining aims to bridge research in the data mining community and software engineering community by providing an open and interactive forum for researchers who are interested in software mining to discuss the methodologies and technical foundations of software mining, approaches and techniques for mining various types of software-related data, applications of data mining to facilitate specialized tasks in software engineering. Participants of diverse background in either data mining or software engineering can benefit from this workshop by sharing their expertise, exchanging ideas and discussing new research results
Authors who are interested in software mining are invited to submit their manuscripts related to all aspects of software mining including software mining foundations, mining specific software data, software mining in specialized tasks, etc.
Topics of interest include but are not limited to:
A. Data mining foundations for software analytics
A1. Emerging machine learning methods for software analytics
A2. Predictive / descriptive modeling techniques for software analytics
A3. Novel frequent pattern mining techniques for software analytics
A4. Evolutionary computing techniques for software analytics
B. Software mining techniques
B1. Software mining models and techniques
B2. Robust and Highly Scalable Algorithms for Mining Large Scale Software System
B3. Understanding and visualizing software mining results
B4. Privacy preserving software mining
C. Mining specific software data
C1. Mining software specifications
C2. Mining source code
C3. Mining execution traces and logs
C4. Mining change patterns and trends
C5. Mining bug and crash reports
C6. Mining natural language artifacts in software data
D. Software mining in specialized tasks
D1. Mining for software defect identification and characterization
D2. Mining for software testing and debugging
D3. Mining for cost/effort estimation
D4. Mining for software development and reuse
D5. Mining for resource allocation
D6. Mining for process control
10月30日
2017
11月03日
2017
初稿截稿日期
初稿录用通知日期
终稿截稿日期
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