8th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering
RAISE'20 is a crossover event where the state of the art in SE+AI is documented and extended. This workshop will explore not only the application of AI techniques to SE problems but also the application of SE techniques to AI problems.
Software has become critical for realizing functions central to our society. For example, software is essential for financial and transport systems, energy generation and distribution systems, and safety-critical medical applications. Software development costs trillions of dollars each year yet, still, many of our software engineering methods remain mostly manual. If we can improve software production by smarter AI-based methods, even by small margins, then this would improve a critical component of the international infrastructure, while freeing up tens of billions of dollars for other tasks. Accordingly, the question that motivates and drives this workshop is: are SE and AI researchers ignoring important insights from AI and SE?.
Human Values in SE for AI (or AI for SE)
Jon Whittle, Monash University, Australia
AbstractIt is now well established that AI systems are often prone to bias, lack of explainability, lack of inclusion, or a lack of consideration of AI's social impact (so called human values). Indeed, governments across the world, as well as private companies, have developed ethical guidelines for the responsible development and use of AI. Unfortunately, these guidelines often remain very high-level and vague and give very few practical suggestions for how software developers should consider values and ethics when building AI systems. In this talk, I will give an overview of our work on operationalizing human values in software - that is, how to start to concretize guidance for embedding values in software.
BiographyJon Whittle is Professor of Software Engineering and Dean of the Faculty of Information Technology at Monash University, Melbourne, Australia. Jon is best known for his work on aspect-oriented modeling, empirical evaluation of model-driven development, and, more recently, social aspects of software engineering. He has been recipient of an IET Software Premium award, a Royal Society Wolfson Merit award, two ten year most influential research awards (at MODELS 2019 and RE 2019, respectively), was a finalist for a Times Higher Education Award for Community Impact, and was listed in the top 15 in the world of impactful SE researchers by JSS in 2019. He was ICSE PC Co-Chair in 2019 and has given recent keynotes at RE 2019 and SANER 2019.
Ensuring quality and dependability of systems based on deep learning
AbstractDeep neural networks are increasingly adopted in safety and business critical domains, such as autonomous driving, financial trading and medical diagnosis. Hence, their dependability and reliability are major concerns, which cannot be addressed by resorting to well established software testing and verification practices. In fact, the root cause of inadequate behaviours of deep learning based systems is quite different from traditional software faults. The approach to handle and resolve such issues deviates also quite substantially from traditional software engineering practices. In this talk, I will discuss the nature of deep learning faults, presenting a fault taxonomy obtained from multiple sources, such as software repository and forum mining, as well as interviews with developers. Then, I will consider the assessment of the quality of deep learning systems, introducing the notion of frontier of behaviours. Finally, I will describe a technique for misbehaviour prediction that aims at anticipating and preventing failures of such systems.
BiographyPaolo Tonella is Full Professor at the Faculty of Informatics and at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He is Honorary Professor at University College London, UK and he is Affiliated Fellow of Fondazione Bruno Kessler, Trento, Italy, where he has been Head of Software Engineering until mid 2018. Paolo Tonella holds an ERC Advanced grant as Principal Investigator of the project PRECRIME. Paolo Tonella wrote over 150 peer reviewed conference papers and over 50 journal papers. His H-index (according to Google scholar) is 56. He is/was in the editorial board of the ACM Transactions on Software Engineering and Methodology, of the IEEE Transactions on Software Engineering, of Empirical Software Engineering, Springer, and of the Journal of Software: Evolution and Process, Wiley. His current research interests are in software testing, in particular approaches to ensure the dependability of machine learning based systems, automated testing of web applications, and test oracle inference and improvement.
Topics of Interest
Topics that might be explored here include (but are not limited to)
- recommendation systems,
- software analytics,
- constraint satisfaction+SE;
- theorem proving+SE;
- model checking+SE;
- genetic algorithms+SE;
- natural languageprocessing+SE;
- cognitive psychology+SE;
- data mining+SE;
- and logic programming+SE
Prospective participants are expected to submit either a regular research paper with late-breaking research results or a research vision/position statement on one or more of the following perspectives:
- Improving SE through AI – including but not limited to knowledge acquisition, knowledge representation, reasoning, agents, machine learning, machine-human interaction, planning and search, optimization, search-based algorithms, natural language understanding, problem solving and decision-making, understanding and automation of human cognitive tasks, AI programming languages, reasoning about uncertainty, new logics, statistical reasoning, software analytics, etc.
- Applying AI to SE activities – including but not limited to requirements, design, software architecture, specification, traceability, program understanding, model-driven development, testing and quality assurance, domain-specific software engineering, adaptive systems, software evolution, etc.
- SE for AI – including but not limited to AI programming languages, program derivation techniques in AI domains, platforms and programmability, software architectures, concurrency, rapid prototyping and scripting for AI techniques, software engineering infrastructure for reflective and self-sustaining systems, etc.
- Deployed Applications of AI or SE – papers that describe a deployed SE application in AI domain or an AI application in SE domain including nut not limited to robotics software development, recommendation systems, API learning, programming in natural language, speech interfaces, digital assistants, etc.
- Mathias Landhäußer, thingsTHINKING GmbH
- Kla Tantithamthavorn, Monash University
- Tingting Yu, University of Kentucky
- Lei Ma, Kyushu University
- Daniel Rodriguez, The University of Alcalá
- Marjan Mernik, University of Maribor
- Richard Torkar, Chalmers and the University of Gothenburg
- Francisco Chicano, University of Málaga
- Alexander Wachtel, Karlsruhe Institute of Technology
- Chunyang Chen, Monash University
- Robert Feldt, Chalmers and the University of Gothenburg
- Markus Wagner, The University of Adelaide
- Justyna Petke, University College London
- Li Li, Monash University
- Tim Menzies, NC State University
- Shin Hong, Handong Global University
- (Workshop Co-Chair) Shin Yoo, Korea Advanced Institute of Science and Technology, South Korea
- (Workshop Co-Chair) Aldeida Aleti, Monash University, Australia
- Burak Turhan, Monash University, Australia
- Leandro Minku, University of Leicester, UK
- Çetin Meriçli, Carnegie Mellon University, USA
- Andriy Miransky, Ryerson University, Canada