The program corresponds to the SingaporeTime (UTC+8) time zone.

Room Info: Education Resource Centre - Ngee Ann Kongsi Auditorium

09:00 - 09.05 - Welcome from the Chairs


09:05 - 10:05 - Keynote 1
Yuriy Brun | Manning College of Information and Computer Sciences at the University of Massachusetts Amherst
Title: The promise and perils of using machine learning when engineering software.

Bio: Yuriy Brun is a professor with the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. His research interests include software fairness, software self-repair, and formal verification. He received his PhD from the University of Southern California in 2008 and was then a Computing Innovation postdoctoral fellow at the University of Washington. Prof. Brun is a recipient of an NSF CAREER Award, a SEAMS Most Influential (test of time) Paper Award, an IEEE Computer Society TCSE New Directions Award, an IEEE TCSC Young Achiever in Scalable Computing Award, a Best Paper Award and four ACM SIGSOFT and SIGPLAN Distinguished Paper Awards, an ACM SIGSOFT Distinguished Artifact Award, a Google Inclusion Research Award, a Google Faculty Research Award, an Amazon Research Award, and a Microsoft Research Software Engineering Innovation Foundation Award. You can learn more about his work from two recent papers: and

Paper Track

10:05 - 10:30 - Paper 1
  • Using Machine Learning to Guide the Application of Software Refactorings: A Preliminary Exploration
    Nikolaos Nikolaidis, Dimitris Zisis, Apostolos Ampatzoglou, Nikolaos Mittas and Alexander Chatzigeorgiou

  • 10:30 - 11:00 - Coffee break

    11:00-11:25 - Paper 2
  • Are Machine Programming Systems Using Right Source-code Measures to Select Code Repositories?
    Niranjan Hasabnis

  • 11:25-11:50 - Paper 3
  • DeepCrash: Deep Metric Learning for Crash Bucketing Based on Stack Trace
    Liu Chao, Xu Yang, Xie Qiaoluan, Li Yong and Choi Hyun-Deok

  • 11:50 - 12:15 - Paper 4
  • On the Application of Machine Learning Models to Assess and Predict Software Reusability
    Matthew Yit Hang Yeow, Chun Yong Chong and Mei Kuan Lim

  • 12:15-12:30 Panel Discussion
    12:30-14:00 Lunch


    14:00 - 15:00 - Keynote 2
    Mike Papadakis | Luxembourg University, Interdisciplinary Centre for Security, Reliability and Trust (SnT)
    Title: Best practices in (empirical) assessment of deep learning testing methods

    Bio: Dr. Mike Papadakis is a senior research scientist at the Interdisciplinary Center for Security, Reliability and Trust (SnT) of the University of Luxembourg. His research interests include software testing, code analysis, machine learning methods for software engineering, and search-based software engineering. He is best known for his work on Mutation Testing for which he has been awarded IEEE TCSE Rising Star Award 2020. He has been awarded several ACM SIGSOFT Distinguished Paper and Artifact Awards and a Facebook Research Award (2019). He has been General Chair of IEEE ICSME 2021 and Program Chair of SSBSE 2021 and IEEE ICST 2022. He also serves at the editorial and review boards of international Software Engineering journals (STVR, Empirical Software Engineering, ACM Transactions on Software Engineering and Methodology) and has (co-)authored more than 100 publications in international peer-reviewed conferences and journals. His work has been supported by Facebook, FNR, CETREL (SIX group company), BGL (BNP Paribas), Microsoft and PayPal.

    Paper Track

    15:00 - 15:25 - Paper 5
  • Neural Language Models for Code Quality Identification
    Srinivasan Sengamedu and Hangqi Zhao

  • Closing

    15:25 - 15:30 - Closing

    15:30 - 16.00 - Coffee break