Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Towards architecture-based self-healing systems
WOSS '02 Proceedings of the first workshop on Self-healing systems
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Enhancing an Artefact Management System with Traceability Recovery Features
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
A language-independent software renovation framework
Journal of Systems and Software - Special issue: Software reverse engineering
A requirements monitoring framework for enterprise systems
Requirements Engineering
Advancing Candidate Link Generation for Requirements Tracing: The Study of Methods
IEEE Transactions on Software Engineering
Software Engineering with Microsoft Visual Studio Team System (Microsoft .NET Development Series)
Software Engineering with Microsoft Visual Studio Team System (Microsoft .NET Development Series)
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
Search Algorithms for Regression Test Case Prioritization
IEEE Transactions on Software Engineering
Clustering support for automated tracing
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
ICPC '08 Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
Improving Architecture-Based Self-Adaptation through Resource Prediction
Software Engineering for Self-Adaptive Systems
Software traceability with topic modeling
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
RELAX: a language to address uncertainty in self-adaptive systems requirement
Requirements Engineering - RE'09 Special Issue; Guest Editor:Kevin T Ryan
We're Finding Most of the Bugs, but What are We Missing?
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Assessing traceability of software engineering artifacts
Requirements Engineering
Application of genetic algorithm and tabu search in software testing
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering
A text-based approach to feature modelling: Syntax and semantics of TVL
Science of Computer Programming
On integrating orthogonal information retrieval methods to improve traceability recovery
ICSM '11 Proceedings of the 2011 27th IEEE International Conference on Software Maintenance
A comparative evaluation of two user feedback techniques for requirements trace retrieval
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Proceedings of the 34th International Conference on Software Engineering
Evolutionary search-based test generation for software product line feature models
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
IEEE Transactions on Software Engineering
Proceedings of the 2013 International Conference on Software Engineering
Searching for better configurations: a rigorous approach to clone evaluation
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Searching for better configurations: a rigorous approach to clone evaluation
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Empirical answers to fundamental software engineering problems (panel)
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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Software traceability is a sought-after, yet often elusive quality in large software-intensive systems primarily because the cost and effort of tracing can be overwhelming. State-of-the art solutions address this problem through utilizing trace retrieval techniques to automate the process of creating and maintaining trace links. However, there is no simple one- size-fits all solution to trace retrieval. As this paper will show, finding the right combination of tracing techniques can lead to significant improvements in the quality of generated links. We present a novel approach to trace retrieval in which the underlying infrastructure is configured at runtime to optimize trace quality. We utilize a machine-learning approach to search for the best configuration given an initial training set of validated trace links, a set of available tracing techniques specified in a feature model, and an architecture capable of instantiating all valid configurations of features. We evaluate our approach through a series of experiments using project data from the transportation, healthcare, and space exploration domains, and discuss its implementation in an industrial environment. Finally, we show how our approach can create a robust baseline against which new tracing techniques can be evaluated.