March 18th, 2019 - Essen, Germany
Held in conjunction with REFSQ’19
Today, the development of software and systems is typically done in a heterogenous environment. Every stage of the development process and workflow involves dedicated tools including requirement management and design solutions, source code management and quality assurance (testing), whereas each tool creates its own development artifacts. This scattered and distributed landscape poses new challenges to satisfy stakeholders needs for making rational decisions, which requires access to all of the artifact sources.
Recent advances in tooling provide basic support to aggregate the massive amount of data from all these tools. However, there is still no efficient method to operationalize the collected information consisting of domain documents, requirements, safety analysis, design, code, test cases, simulations, version control data, fault logs, model checkers, project plans and so on. When combined, e.g. through means of software analytics, this data can deliver precise answers to questions stakeholders and business analysts demand and support the decision-making process. Advances in machine learning, especially in the field of (deep) neural networks, allow to process the data and provide the foundation to develop intelligent systems on top of it.
Next to further improving the data collection process (repository mining), current research activities focus on efficient methods to trace between the artifacts, formulate queries to access the data, and reporting techniques to present retrieved results. Novel machine learning algorithms offer new ways to aggregate and process artifact data providing insights and deeper understanding when analyzing requirements.
Goal The purpose of the workshop is to provide a platform for researchers and practitioners to share and exchange ideas and experiences about intelligent data science in software projects. Researchers will explain their ideas how to solve key aspects in the existing challenges. On the other hand, practitioners can provide feedback on the ideas and describe how they currently process business data and gather information in their day to day work. The dialog offers a great opportunity to develop objectives for next generation methods and systems.
The workshop combines several topics, including but not limited to
Short papers 3-6 pages.
The authors should state the position to any of the workshop topics. The paper should reflect on past attempts (success or failure) to solve (sub)problems on the respective topic. Papers also can describe emerging ideas how to apply data science methods in distributed workflow systems. Applied uses cases and experiments in industrial environments are highly welcomed.
The workshop organizer especially encourage practitioners to contribute by reporting how they currently address the initially formulated questions and satisfy their information needs in making decisions. Such a submission may report about the applied workflow and methods in their daily work.
Each paper will be reviewed by three members of the program committee. Accepted papers will appear in the REFSQ’19 workshop proceedings and be presented at the workshop.
Submitted papers are evaluated based on relevance, originality and soundness of argumentation.
Contributions should be formatted using CEUR Style for one-column: onecolceurws.sty from http://ceur-ws.org/Vol-XXX/samplestyles/. An example is paper1.tex. Also, the paper title should use the title format with capitalization for emphasis (e.g.; Filling a PUT-FORM by Autocompletion)
Please submit the papers in PDF format on EasyChair.
ATLANTIC Congress Hotel Essen Messeplatz 3, 45131 Essen, Germany
|DSML4RE||Monday, March 18|
|0900 – 0930||Introduction / Keynote|
|0930 – 1030||Paper Session 1|
|1045 – 1145||Paper Session 2 / Tool demonstration|
|1145 - 1230||Interactive discussion|
Technische Universität Ilmenau
Technische Universität Ilmenau