@inproceedings{97,
  abstract     = {{Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.}},
  author       = {{van Rooijen, Lorijn and Bäumer, Frederik Simon and Platenius, Marie Christin and Geierhos, Michaela and Hamann, Heiko and Engels, Gregor}},
  booktitle    = {{2017 IEEE 25th International Requirements Engineering Conference Workshops (REW)}},
  isbn         = {{978-1-5386-3489-9}},
  keywords     = {{Software, Unified modeling language, Requirements engineering, Ontologies, Search problems, Natural languages}},
  location     = {{Lisbon, Portugal}},
  pages        = {{379--385}},
  publisher    = {{IEEE}},
  title        = {{{From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process}}},
  doi          = {{10.1109/REW.2017.26}},
  year         = {{2017}},
}

@inproceedings{36994,
  abstract     = {{This paper proposes a quality driven, simulation based approach to functional design verification, which applies mainly to IP-level HDL designs with well specified test instruction format and is evaluated on a soft microprocessor core MB-LITE [5]. The approach utilizes mutation analysis as the quality metric to steer an automated simulation data generation process. It leads to a simulation flow with two phases towards an enhanced mutation analysis result. First in a random simulation phase, an in-loop heuristics is deployed and adjusts dynamically the test probability distribution so as to improve the coverage efficiency. Next, for each remaining hard-to-kill mutant, a search heuristics on test input space is developed to iteratively locate a target test, using a specific objective cost function for the goal of killing HDL mutant. The effectiveness of this integrated two-phase simulation flow is demonstrated by the results with the MB-LITE microprocessor IP.}},
  author       = {{Xie, Tao  and Müller, Wolfgang and Letombe, Florian}},
  booktitle    = {{Proceedings of SOCC2012}},
  keywords     = {{Analytical models, Hardware design languages, Microprocessors, Cost function, Data models, Search problems, IP networks}},
  publisher    = {{IEEE}},
  title        = {{{Mutation-Analysis Driven Functional Verification of a Soft Microprocessor}}},
  doi          = {{10.1109/SOCC.2012.6398362}},
  year         = {{2012}},
}