Keynote Lectures

Data-Driven Requirements Engineering: The Way Ahead
Xavier Franch, Universitat Politècnica de Catalunya, Spain, Spain

Subjective Databases
Alon Halevy, Facebook AI, United States, United States

Software Similarities and Clones: A Curse or Blessing?
Stanislaw Jarzabek, Bialystok University of Technology, Poland, Poland

Data-Driven Requirements Engineering: The Way Ahead

Xavier Franch
Universitat Politècnica de Catalunya, Spain
http://www.essi.upc.edu

Brief Bio
Xavier Franch is professor at the Universitat Politècnica de Catalunya (UPC-BarcelonaTech), where he leads the GESSI research group (https://gessi.upc.edu/en). Active researcher with more than 200 peer-reviewed publications, his research interests include Requirements Engineering, System Modeling, Software Evolution and Adaptation, and Agile Software Development, among others. In the EU framework programmes, he coordinated the Q-Rapids (H2020, 2016-2019) and RISCOSS (FP7, 2012-2015) projects, acted as scientific manager in SUPERSEDE (H2020, 2015-2018) and participated in OpenReq (H2020, 2017-2019). He is editorial board member of the following journals: IST (Elsevier), REJ and Computing (Springer), and IJCIS (World Scientific); as well as Deputy Editor of IET Software and Journal First chair in JSS (Elsevier). He belongs to the steering committee of several major conferences (remarkably IEEE RE and CAiSE) and has occupied several conference positions in major software engineering conferences conferences, remarkably as general chair (PROFES'9 and RE’08) and program chair (RE’16, ICSOC’14, CAiSE’12 and REFSQ'11). He is full member of the International Requirements Engineering Board (IREB) association (also as part of the organization’s Council). He has won several best papers awards. He has taught tutorials and organized workshops on software engineering-related topics in several major conferences as ICSE, RE, CAiSE. More details at https://www.essi.upc.edu/~franch/.

Abstract
Data-driven requirements engineering is becoming increasingly widespread in the development of today's software systems, services and apps. The exploitation of data coming from the user through several sources may indeed become an extremely useful input to requirements elicitation and management, but it does not come for free. Techniques such as NLP and ML are difficult to master and require high-quality data, whilst their generalization remains a challenge. Also, understanding the consequences into the companies' development practices is still an open issue. In this keynote, I summarize the main concepts behind data-driven requirements engineering, then I provide an overview of the state of the art, recapitulate lessons learned and open challenges, and outline future research areas especially related to the impact of this approach into the full software development process.

Subjective Databases

Alon Halevy
Facebook AI, United States

Brief Bio
Alon Halevy joined Facebook AI in August, 2019. Until December, 2018, Alon was the CEO of Megagon Labs where his team focused on developing AI for well-being. Before that, Alon led the Structured Data Research Group at Google for 10 years. Previously, he was a professor of computer science at the University of Washington, where he founded the database research group. Alon is a founder Nimble Technology, and of Transformatic, Inc., which was acquired by Google in 2005. He is the author of "The Infinite Emotions of Coffee" and co-author of "Principles of Data Integration". Alon is an ACM Fellow, received the Sloan Fellowship and the Presidential Early Career Awards for Scientists and Engineers (PECASE) Award. He received his Ph.D. in Computer Science from Stanford University in 1993 and his bachelor’s degree from the Hebrew University of Jerusalem.

Abstract
Online consumers are constantly seeking experiences, such as vacations, restaurant outings and exciting jobs in order to improve their well-being. However, e-commerce search engines only support searches for experiences to a very limited extent -- you can search on the objective attributes of a service (e.g., hotel price and location), but the experiential aspects are buried in online reviews. E-commerce sites make some effort to surface comments from reviews, but users can still not specify experiential aspects (e.g., romantic hotel in a quiet Mediterranean town) in their queries. There has been considerable work in the NLP community to recognize and extract subjective text, but that’s only the first step towards querying.
To address this challenge, we introduce OpineDB, a subjective database. OpineDB is based on a data model that carefully balances the richness and bottom-up nature of natural language and the top-down design principles of databases. OpineDB is able to answer queries that combine multiple subjective conditions and aggregate subjective data. Unlike a traditional database system, there may not be a 1-1 mapping between query terms and the database schema. In some cases, OpineDB needs to find the closest attribute (or combination of attributes) that answers a user query, and in some cases it may have to fall back to retrieval directly from the review text.
Joint work with Yuliang Li, Jinfeng Li, Vivian Li, Aaron Feng, Saran Mumick and Wang-Chiew Tan from Megagon Labs.

Software Similarities and Clones: A Curse or Blessing?

Stanislaw Jarzabek
Bialystok University of Technology, Poland

Brief Bio
Stan Jarzabek has been working on techniques for software reuse since 1997. His team developed XVCL (XML-based Variant Configuration Language), a variability management technique for software reuse in 2000. Since then, XVCL has been applied in lab studies and industrial projects with results published at major software engineering forums (a study of redundancies in Buffer library with XVCL won ACM Best Paper Award; industrial projects with XVCL were published at ICSE and FSE). XVCL later evolved to a more flexible system called ART (Adaptive Reuse Technique) http://art-processor.org. Stan’s long-term research interest is software engineering (software reuse and maintenance), and in recent years mHealth – use of mobile technology to improve delivery of healthcare. Stan received MSc and PhD from Warsaw University. He has been a Professor at Bialystok University of Technology since 2015; in 1992-2015 he was an Associate Professor at the Department of Computer Science, National University of Singapore; in 1990-92 he was a Research Manager of CSA Research Ltd in Singapore. Before, Stan taught at McMaster University, Canada and worked for industrial research institute in Warsaw.

Abstract
Similarities are inherent in software - Who has not adapted existing code to speed up writing new programs? While simplistic and not be very effective in long run, copy-paste-modify is a common reuse practice. It produces software clones - recurring in variant forms similar code fragments, classes, source files or even bigger program modules. Software cloning phenomenon has been investigated by researchers for decades. Whether clones are good or bad depends on the context. Sometimes software clones hinder program understanding and maintenance, and are considered a sign and measure of decaying software structure and quality. Such software clones should be avoided or eliminated, whenever possible. In other situations, software clones are created intentionally and play some useful role in a program. Application of company standards and design patterns leads to clones, but we do not question the value of these practices because of that. In the talk, I will analyse the multifaceted phenomenon of software similarities, particularly focusing on the situations when software clones - not necessarily good - explode because we can’t contain their explosion with conventional programming techniques.
Software systems can comprise 10’s of millions LOC (WINDOWS is well over 100 millions LOC), with thousands of inter-related components, reaching the limits of what today’s technology can handle. We’ll be surely challenged by even larger and more complex systems-of-systems of the future. How do we cope with such systems if their complexity grows proportionally to their size? Especially software maintenance, which is almost exclusively done at the level of code, exposes developers to such complexity. Not surprisingly, up to 80% of software costs go to maintenance. I will present a view that software similarities have yet unexploited potential to help us reduce software complexity. But to tap on that potential we must reach beyond the current software reuse paradigm.