Applied software measurement: assuring productivity and quality
Applied software measurement: assuring productivity and quality
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
ER '02 Proceedings of the 21st International Conference on Conceptual Modeling
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Machine Learning
Development of a hybrid cost estimation model in an iterative manner
Proceedings of the 28th international conference on Software engineering
Proceedings of the 2006 international workshop on Mining software repositories
Classifying Software Changes: Clean or Buggy?
IEEE Transactions on Software Engineering
Reasoning about agents and protocols via goals and commitments
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A goal-based framework for contextual requirements modeling and analysis
Requirements Engineering
Analytics for software development
Proceedings of the FSE/SDP workshop on Future of software engineering research
Modeling and reasoning about service-oriented applications via goals and commitments
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
On-demand feature recommendations derived from mining public product descriptions
Proceedings of the 33rd International Conference on Software Engineering
Goldfish bowl panel: software development analytics
Proceedings of the 34th International Conference on Software Engineering
Requirements-Driven root cause analysis using markov logic networks
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Towards advanced goal model analysis with jUCMNav
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
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The life cycle activities of industrial software systems are often complex, and encompass a variety of tasks. Such tasks are supported by integrated environments (IDEs) that allow for project data to be collected and analyzed. To date, most such analytics techniques are based on quantitative models to assess project features such as effort, cost and quality. In this paper, we propose a project data analytics framework where first, analytics objectives are represented as goal models with conditional contributions; second, goal models are transformed to rules that yield a Markov Logic Network (MLN) and third, goal models are assessed by an MLN probabilistic reasoner. This approach has been applied with promising results to a sizeable collection of software project data obtained by ISBSG repository, and can yield results even with incomplete or partial data.