PhD: Discovering Behavioral Software Models from Software Event Data (V32.2142) at Eindhoven University of Technology
Project In the context of the 3TU.BSR "Big Software on the Run" research program we are looking for 6 PhDs and 3 Postdocs interested in Software Analytics and Data Science, including a PhD candidate working on the interplay between process mining and software engineering. The PhD project will link data science and process science. The 3TU.BSR project will run for a period of four years and is supported by the three Dutch technical universities (Eindhoven University of Technology, TU Delft, and University of Twente). It was initiated by 3TU.NIRICT, the Netherlands Institute for Research on ICT, which comprises all ICT research of the three universities of technology in the Netherlands. The PhD positions will run for 4 years. The three postdocs will be appointed for approximately 3 years. Context Millions of lines of code - written in different languages by different people at different times, and operating on a variety of platforms - drive the systems performing key processes in our society. The resulting software needs to evolve and can no longer be controlled a priori as is illustrated by a range of software problems. The 3TU.BSR research program will develop novel techniques and tools to analyze software systems in vivo - making it possible to visualize behavior, create models, check conformance, predict problems, and recommend corrective actions. To deal with Big Software on the Run (BSR), we propose to shift the main focus from a priori software design to a posteriori software analytics thereby exploiting the large amounts of event data generated by today's systems. The core idea is to study software systems in vivo, i.e., at runtime and in their natural habitat. We would like to understand the actual (desired or undesired) behavior of software. Running software needs to adapt to evolving and diverging environments and requirements. This forces us to consider software artifacts as "living organisms operating in changing ecosystem". This paradigm shift requires new forms of empirical investigation that go far beyond the common practice of collecting error messages and providing software updates. To take on this ambitious task leading Dutch groups in Data Science and Software Science have joined forces in the 3TU.BSR "Big Software on the Run" program. PhD Position: Automatically Discovering Behavioral Software Models from Software Event Data There are six PhD positions. In this advertisement we focus on the PhD position supervised by Wil van der Aalst (TU/e) & Ari van Deursen (TUD). The PhD will join the Architecture of Information Systems (AIS) group at Eindhoven University of Technology and focus on the application of process mining to software-related event data. AIS is the globally leading research group in the exciting new field of process mining (www.processmining.org<http://www.processmining.org>). Process mining techniques focus on process discovery (extracting process models from event logs), conformance checking (comparing normative models with the reality recorded in event logs), and extension (extending models based on event logs). The work resulted in the development of the ProM framework that is widely used in industry and serves as a platform for new process mining techniques used by research groups all over the globe. Moreover, many of the techniques developed in the context of ProM have been embedded in commercial tools. See also www.processmining.org<http://www.processmining.org/>. Process mining is an amazing new research field and is rapidly gaining recognition as the missing link between model-driven and data-driven approaches. The PhD will apply existing process mining techniques to events generated by software systems. Process models and user interface workflows underlie the functional specification of almost every substantial software system. However, these are often left implicit or are not kept consistent with the actual software development. When the system is utilized, user interaction with the system can be recorded in event logs. After applying process mining methods to logs, we can derive process and user interface workflow models. These models provide insights regarding the real usage of the software and can enable usability improvements and software redesign. In this project, we aim to develop process discovery techniques specific for software. How can domain knowledge and software structure be exploited while mining? How to discover software patterns and anti-patterns? Job requirements Requirements We are looking for candidates that meet the following requirements: · a solid background in Computer Science, Data Science, or Software Science (demonstrated by a relevant Master); · ideal candidates have a strong background in both software engineering and process/data mining; also candidates that have a strong background in one of the two areas can apply but should demonstrate that they are interested to become an expert in both; · candidates from non-Dutch or non-English speaking countries should be prepared to prove their English language skills; · good communicative skills in English, both in speaking and in writing; · candidates are expected to realize research ideas in terms of prototype software, so software development skills are needed. Note that we are looking for candidates that really want to make a difference and like to work on things that have a high practical relevance while having the ambition to compete at an international scientific level (i.e., present at top conferences and in top journals). Conditions of employment Appointment and salary We offer: · A full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months; · A gross salary of € 2083 per month in the first year increasing up to € 2664 in the fourth year; · Support for your personal development and career planning including courses, summer schools, conference visits etc.; · A broad package of fringe benefits (e.g. excellent technical infrastructure, child daycare and excellent sports facilities). Information and application More information: · For more information about this position contact prof.dr.ir. Wil van der Aalst (Full Professor), e-mail: [email protected]<mailto:[email protected]> or by telephone: +31 40 247 4295. · For more information about the employment conditions contact drs. Charl Kuiters (HR advisor), e-mail: [email protected]<mailto:[email protected]> or by telephone: +31 40 247 2321. The application should consist of the following parts: · Cover letter explaining your motivation and qualifications for the position (the letter should show an understanding of process mining and the work done within AIS, see websites such as www.processmining.org<http://www.processmining.org> and the book "Process Mining: Discovery, Conformance and Enhancement of Business Processes"); · Detailed Curriculum Vitae; · List of courses taken at the Bachelor and Master level including marks; · Names of at least three referees. Please apply via http://jobs.tue.nl/nl/vacature/phd-discovering-behavioral-software-models-from-software-event-data-206118.html. Use the "Apply now" button of Vacancy V32.2142. Applications via e-mail will not be accepted. Currently there are several open positions related to process mining and data science. · Postdoc: Process Analytics for the European Data Science Academy http://jobs.tue.nl/nl/vacature/postdoc-process-analytics-for-the-european-data-science-academy-206483.html · PhD on Process Mining for Predictive Models in Healthcare Smart Maintenance http://jobs.tue.nl/nl/vacature/phd-on-process-mining-for-predictive-models-in-healthcare-smart-maintenance-206476.html · Postdoc: Software Analytics and Process Mining http://jobs.tue.nl/nl/vacature/postdoc-software-analytics-and-process-mining-206130.html · PhD: Discovering Behavioral Software Models from Software Event Data http://jobs.tue.nl/nl/vacature/phd-discovering-behavioral-software-models-from-software-event-data-206118.html · Assistant Professor in Data Science (0.8-1.0 fte) http://jobs.tue.nl/nl/vacature/assistant-professor-in-data-science-0810-fte-205716.html Please consult the web pages to get more information and apply.
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