近年、機械学習・深層学習の発展によって、機械学習を利用するシステムが急速に社会に広がって1きていますが、開発・テスト・運用の方法論はまだ確立できていません。そこで、従来の「ソフトウェア工学」に対応するような「機械学習工学」について議論すべく機械学習工学研究会が設立されました。

本セミナーでは、カナダに立ち上がった機械工学アプリケーションのためのソフトウェア工学イニシアチブ(SEMLA:Software Engineering for Machine-Learning Applications)の設立者のGiuliano Antoniol教授とFoutse Khomh准教授をお招きし、SEMLAの現状と最新の研究を講演いただきます。 さらに、スマートエスイーと連携し、ソフトウェア開発に機械学習を活用する研究についても紹介いたします。

なお、招待講演はすべて英語での発表となり、同時翻訳は行いません。

招待講演

• 招待講演1: Giuliano Antoniol, Professor of Ecole Polytechnique de Montréal, Canada: "Software Engineering for Machine-Learning Applications: The Road Ahead"

• 招待講演2: Foutse Khomh, Associate Professor of Ecole Polytechnique de Montréal, Canada: "The open-closed principle of modern machine learning frameworks"

• 招待講演3: Hongyu Zhang, Associate Professor of The University of Newcastle, Australia: "Towards Effective Code Reuse by Searching"

講演内容は変更になる可能性があります。 また、招待講演の他にMLSEおよびスマートエスイーの活動の紹介を予定しています。

講演内容

Speaker: Giuliano Antoniol

Title: Software Engineering for Machine-Learning Applications: The Road Ahead

Abstract: The First Symposium on Software Engineering for Machine Learning Applications (SEMLA) aimed to create a space in which machine learning (ML) and software engineering (SE) experts could come together to discuss challenges, new insights, and practical ideas regarding the engineering of ML and AI-based systems. Key challenges discussed included the accuracy of systems built using ML and AI models, the testing of those systems, industrial applications of AI, and the rift between the ML and SE communities. This talk reports on the first SEMLA installment, discusses past contributions and presents future challenges.

Biography: Giuliano Antoniol (Giulio) worked in companies, research institutions and universities such as the Fondazione Bruno Kessler (FBK, forerly IRST), Trento (Italy) and the University of Sannio, Italy. In 2005 he joined the Polytechnique Montreal and was awarded the Canada Research Chair Tier I in Software Change and Evolution. He has participated in the program and organization committees of numerous IEEE-sponsored international conferences. He served as program chair, industrial chair, tutorial, and general chair of international conferences and workshops including IEEE International Conference on Software Testing, Verification and Validation and the Search Based Software Engineering Track (SBSE) of the Evolutionary Computation Conference. He is a member of the editorial boards of the Journal of Software Testing Verification & Reliability, the Software Quality Journal and the Journal of Software Maintenance and Evolution: Research and Practice. Dr Giuliano Antoniol served as Deputy Chair of the Steering Committee for the IEEE International Conference on Software Maintenance and International Symposium on Search-Based Software Engineering (SSBSE). He contributed to the program committees of more than 30 IEEE and ACM conferences and workshops, and he acts as referee for all major software engineering journals. He is currently Full Professor at the Polytechnique Montreal, where he works in the area of software evolution, empirical software engineering, software traceability, search based software engineering and software testing.

Speaker: Foutse Khomh

Title: The open-closed principle of modern machine learning frameworks

Abstract: Recent advances in computing technologies and the availability of huge volumes of data have sparked a new machine learning (ML) revolution, where almost every day a new headline touts the demise of human experts by ML models on some task. Open source software development is rumoured to play a significant role in this revolution, with both academics and large corporations such as Google and Microsoft releasing their ML frameworks under an open source license. This talk takes a step back to examine and understand the role of open source development in modern ML, by examining the growth of the open source ML ecosystem on GitHub, its actors, and the adoption of frameworks over time.

Biography: Foutse Khomh is an associate professor at Polytechnique Montréal, where he heads the SWAT Lab on software analytics and cloud engineering research (http://swat.polymtl.ca/). He received a Ph.D in Software Engineering from the University of Montreal in 2010, with the Award of Excellence. His research interests include software maintenance and evolution, cloud engineering, service-centric software engineering, empirical software engineering, and software analytic. He has published several papers in international conferences and journals, including ICSM(E), ASE, ISSRE, SANER, ICWS, HPCC, IPCCC, JSS, ASEJ, JSEP, EMSE, and TSE. His work has received two ten-year Most Influential Paper (MIP) Award, and four Best/Distinguished paper Awards. He has served on the program committees of several international conferences including ICSM(E), SANER, MSR, ICPC, SCAM, ESEM and has reviewed for top international journals such as SQJ, JSS, EMSE, TSE and TOSEM. He is program chair for Satellite Events at SANER 2015, program co-chair of SCAM 2015, ICSME 2018, and ICPC 2019, and general chair of ICPC 2018. He is one of the organizers of the RELENG workshop series (http://releng.polymtl.ca) and Associate Editor for IEEE Software.

Speaker: Hongyu Zhang

Title: Towards Effective Code Reuse by Searching

Abstract： Over years of software development, an organization can accumulate a large amount of source code. Open source repositories such as GitHub and CodePlex also enable unprecedented access to a vast corpus of source code. Developers can reuse these code to improve their software development productivity. For example, they can search a codebase to understand how a functionality is implemented and how an API is used. Through code reuse, developers can accomplish tasks that were previously difficult or time-consuming. In this talk, I will introduce some of my work on effective code reuse, including natural language based code search and deep learning based code search.

Biography: Hongyu Zhang is currently an Associate Professor at The University of Newcastle, Australia. Previously, he was a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received the PhD degree from National University of Singapore in 2003. His research is in the area of Software Engineering, in particular, software analytics, testing, maintenance, and reuse. The main theme of his research is to improve software quality and productivity by mining software data. He has published more than 120 research papers in international journals and conferences, including TSE, TOSEM, ICSE, FSE, POPL, AAAI, IJCAI, KDD, ASE, ISSTA, ICSM, ICDM, and USENIX. He received two ACM Distinguished Paper awards. He has also served as a program committee member for many software engineering conferences. More information about him can be found at: https://sites.google.com/site/hongyujohn/ .

当日の受付について

• 時間：13:00 開場です。
• 場所：国立情報学研究所 12階会議室1208
  • 東京メトロ半蔵門線，都営地下鉄新宿線・三田線「神保町」駅 徒歩5分
  • 東京メトロ東西線「竹橋」駅 徒歩5分

主催・共催

主催：機械学習工学研究会（MLSE）、 共催：スマートエスイー

コミュニティについて

Slack上で情報共有や議論をしていますので、ぜひ下記URLからご登録ください。

https://mlxse.herokuapp.com/

注意事項

• 会場は禁煙です。
• Free WiFiは用意されていませんのでご了承ください。各キャリアのホットスポットがご利用いただけます。

会場への案内

国立情報学研究所1階入り口にて身分証を提示の上、12階 会議室に直接お越しください。