No. 5 - Software Documentation and Software Economics for Knowledge Sharing
19 August 2020 13:00 - 14:00, Online via Zoom (meeing ID will be announced soon) and Facebook Live (SERU page)
Title: Software Documentation and Software Economics for Knowledge Sharing
Abstract: Traceability between knowledge/information and its implementation is essential in software maintenance. Pieces of source code are connected with external resources, such as Stack Overflow threads, discussions of issues, academic papers for algorithms. These external resources are sometimes mentioned in source code comments. From our empirical studies on software documentation, we have observed some issues including link decay, obsolete knowledge, etc. Since knowledge and information is not limited to single software development projects, these issues are spreading over software ecosystems. To address these challenges, we are considering economic mechanisms, similar to bug bounty programs. This talk will explore our previous findings and our current and future challenges.
Bio: Hideaki Hata is an assistant professor at the Nara Institute of Science and Technology. His research interests include software ecosystems, human capital in software engineering, and software economics. He received a Ph.D. in information science from Osaka University. More details of his work can be found at https://hideakihata.github.io/.
No. 4 - Artificial Intelligence and Software Engineering (AI4SE and SE4AI)
3 January 2020 10:00 - 11:30, Room IT 405, ICT Building, Mahidol University (Salaya)
Title: Artificial Intelligence and Software Engineering (AI4SE and SE4AI)
Abstract: As software products become pervasive in all areas of our society, building high-quality software in a productive manner becomes crucial to the software industry. The rise of Artificial Intelligence (AI) is potentially a game changer in improving software quality, accelerating productivity and increasing project success rates. This talk will discuss how AI can provide the capabilities to assist software engineering teams in many aspects, from automating routine tasks to providing project analytics and actionable recommendations, and even making decisions (AI for Software Engineering). This talk will also explore some of the new challenges for software engineering in developing large scale AI-based systems (Software Engineering for AI).
Bio: Hoa Khanh Dam is Associate Professor in the School of Computing and Information Technology, University of Wollongong (UOW) in Australia. He is Associate Director for the Decision System Lab at UOW, heading its Software Analytics research program. His research interests lie primarily in the intersection of Software Engineering and Artificial Intelligence (AI). He develops AI solutions for project managers, software engineers, QA and security teams to improve software quality/cybersecurity and accelerate productivity. His research also focuses on methodologies and techniques for engineering autonomous AI multi-agent systems. More details of his work can be found at https://www.uow.edu.au/~hoa/.
No. 3 - New Trends in Software Engineering
6 December 2019 15:00 - 16:30, Room IT 405, ICT Building, Mahidol University (Salaya)
This is a special seminar for our 4th year students who are going to present their work at the 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019).
Their work are the results from their summer internship at the SE lab at Nara Institute of Science and Technology (NAIST) and University of Bremen that tackle important and emerging trends in software engineering.
Software Team Member Configurations: A Study of Team Effectiveness in Moodle
Visualizing the Usage of Pythonic Idioms Over Time: A Case Study of the with open Idiom
Improving Clone Detection Precision using Machine Learning Techniques
How Do Contributors Impact Code Naturalness? An Exploratory Study of 50 Python Projects
No. 2 - Intepretable Artificial Intelligence
17 June 2019 14:00 - 15:00, Room IT 405, ICT Building, Mahidol University (Salaya)
Title: Interpretable Artificial Intelligence
Abstract: Interpretable artificial intelligence (IAI) refers to techniques which can be trusted and easily understood by humans. Unlike the concept of ‘black box’, IAI can be used to implement a social right to explanation i.e. to explain why an AI arrives to a specific decision. A technical challenge of explaining AI decisions is known as the interpretability problem. One possible approach for handling it is to carefully design and develop AI w.r.t. formal syntax and semantics. In this talk, we will introduce the basics of computational logic (the notions of syntax, semantics, and proof theory) and its relationship to knowledge representation formalisms. We will also investigate the standard inferences and relate their computations to the transparency of AI decisions.
Bio: Teeradaj Racharak is an assistant professor in the field of artificial intelligence at Japan Advanced Institute of Science and Technology (JAIST). Prior to that, he did his Ph.D. in description logic under the supervision of professor Satoshi Tojo and his master in logic programming under the supervision of professor Phan Minh Dung. Apart from his studies in computational logic, he is an open-minded software engineer and interested in many things related to artificial intelligence and software development methodologies. His research interest is centered on formal development toward human intelligence i.e. how a machine can ‘learn’ and ‘reason’ like a human? In particular, he has been studying to address the following research areas: (1) machine learning, (2) computational logic, and (3) their applications to natural language understanding.
No. 1 - The future of software defect prediction and software quality
11 June 2019 13:30 - 14:30, Room IT 405, ICT Building, Mahidol University (Salaya)
Title: Explainable Artificial Intelligence to Predict Future Software Defects
Abstract: With the rise of software systems ranging from personal assistance to nation’s facilities, software defects become more critical concerns as they can cost millions of dollar as well as impact human lives. Yet, at the breakneck pace of rapid software development settings (like Agile/DevOps paradigm), the Quality Assurance (QA) practices nowadays are still time-consuming. Currently, I’m leading a research program to develop AI to predict future software defects and explain why they are defective in order to optimize the limited QA resources and develop the most effective quality improvement plans. This research project is expected to provide significant benefits including the reduction of software defects and operating costs, while accelerating development productivity.
Bio: Dr. Chakkrit (Kla) Tantithamthavorn is an Assistant Professor in Faculty of Information Technology, Monash University, Melbourne, Australia (a research-intensive university in the World’s Top 100 Universities). His current research focuses on Explainable AI in Software Engineering. His work has been published at top-tier software engineering venues, such as IEEE Transactions on Software Engineering (TSE), Empirical Software Engineering (EMSE), and the International Conference on Software Engineering (ICSE). Please contact me if you want to do world-class research on the development of AI technologies to improve software quality and productivity.
Abstract: Software is a byproduct of human activities. To strive for the successful completion of a software product, software development requires deep collaboration and interactions among software practitioners, especially for globally-distributed software development teams. Due to the continuously growing size of development teams and software products, collaboration management becomes a crucial concern. For example, poor collaboration in software development processes may lead to poor software quality. However, good practices still remains an elusive goal. Therefore, Dr Thongtanunam’s research focuses on incorporating various sources of development activities, gleaning actionable insights for software engineering management, and providing tool support for software practitioners with the aim of improving software quality. In this talk, Dr Thongtanunam will present her empirical studies which highlight the impact of code review practices on software quality and discuss some of her proposed tool support (e.g., a reviewer recommendation algorithm).
Bio: Dr Patanamon Thongtanunam is a lecturer at School of Computing and Information Systems (CIS), the University of Melbourne (The number 1 university in Australia and Top 32 university in the world). Her primary research goals are directed towards data-driven software engineering, i.e., uncovering empirical evidence and extracting knowledge from data recorded in software repositories by using statistical analysis. Her research has been published at top-tier software engineering venues like International Conference on Software Engineering (ICSE) and Journal of Empirical Software Engineering (EMSE). More about Dr Thongtanunam and her work is available online at http://patanamon.com.