The International Workshop on Machine Learning and Software Testing (MLST 2019) seeks to bring together researchers and practitioners to exchange and discuss the most recent synergistic machine learning (ML) and software testing (ST) techniques and practices. With the recent tremendous success of machine learning in many cutting-edge applications, machine learning has become a key driving force of the next-generation innovated technology. However, the quality assurance of ML software is still at a very early stage. This year’s MLST centers around two key scopes to bring researchers with diverse background (e.g., ST, AI, Security) to come up with in-depth discussion and solutions for both ML and ST: (1) How to better take the advantage for ML to further improve ST, and (2) How to better test and analyze ML towards creating ML software with better quality. As we see that machine learning has already significantly contributed to SE communities, with some initial work to ST. On the other hand, ST for ML is still at a very early stage.
MLST 2019 will, therefore, be a workshop, which seeks to develop a cross-domain community that systematically looks into both areas from the new perspective. The workshop will explore not only how we apply machine learning to ST, but also the emerging ST techniques and tools for assessing, predicting, and improving the safety, security, quality, and reliability of machine learning based software that in turn help the development of artificial intelligence. We hope MLST could facilitate to create intelligent software with high quality, as well as accelerate the process of software development and quality assurance with intelligence.
Theme: The theme of the workshop is to leverage traditional software testing (ST) to better understand machine learning based software and draw strong connections between the two. We aim to apply ST to guide test case generation for machine learning software, as well as use machine learning techniques to scale up ST tasks with higher intelligence.
Goals: Our main goal is to shed light on the direction of applying the principles of software testing to machine learning and therefore evaluate the robustness of machine learning especially deep learning software. The workshop also aims to leverage machine learning techniques to advance the efficiency, accuracy, effectiveness, and usefulness of current ST techniques.
Relevance: The audience of ICST focuses on general software test generation, which is highly related and would be benefited by leveraging machine learning techniques to accelerate the process. In addition, it would be helpful for the ICST community to know how to apply current software testing generation principles to guide testing generation for machine learning software and make a broader impact to the community.
Artificial Intelligence, and especially Machine Learning, is being rapidly adopted by various software systems, including safety critical systems such as autonomous driving and medical imaging. This calls for an urgent need to test these systems, but the task can be very different from testing of traditional softweare systems. What can we, softeware testing researchers, bring to these challenges? We will briefly examine various testing techniques that have been applied to AI systems so far, and survey the problem landscape to highlight areas that need further exploration.
There is increasing interest in machine learning (ML) techniques and their applications. With machine learning techniques, we generate the system behavior in an inductive way from training data. This shift of software development paradigm introduces unique challenges in quality assurance. ML-based systems often accompany other difficulties, such as with unbounded requirements and environments in the open world. In this talk, I overview and discuss challenges in quality assurance of ML-based systems. I try to take a wide-ranging perspective, not limited to the recent active testing research, referring to our recent activities with the Japanese industry, including questionnaire surveys as well as construction of guidelines. Thus, I invite testing researchers to much further extend the research area for testing ML-based systems.
|April 23, 2019|
|8:50 - 10:00||Opening
Keynote Talks (60 mins)
Testing of AI Systems - Challenges Ahead.
Shin Yoo, Associate Professor, Korea Advanced Institute of Science and Technology, Korea
|10:00 - 10:30||Coffee Break|
|10:30 - 11:40||
Challenges in quality assurance for machine learning-based systems. (Academic Invited Talk, 45 mins)
Fuyuki Ishikawa, Associate Professor, National Institute of Informatics
Learning to Restrict Test Range for Compiler Testing. (Industrial Invited Talk, 25 mins)
Junhua Zhu, Compiler senior test engineer, Huawei
|11:40 - 11:45||Short Break|
|11:45 - 12:30||
Coverage-guided Learning-assisted Grammar-based Fuzzing. (15 mins)
Yuma Jitsunari and Yoshitaka Arahori.
Learning Performance Optimization from Code Changes for Android Apps. (15 mins)
Ruitao Feng, Guozhu Meng, Xiaofei Xie, Ting Su, Yang Liu and Shang-Wei Lin.
Variable Strength Combinatorial Testing for Deep Neural Networks. (10 mins)
Yanshan Chen, Ziyuan Wang, Dong Wang, Chunrong Fang and Zhenyu Chen.
|12:30 - 13:30||Lunch|
MLST workshop aims to cover the interdisciplinary topics as it relates to both ML and ST. Prospective participants are expected to focus on recent progress, breakthroughs in ML and ST, or a research vision/position statement, or industrial relevance, empirical studies as well as experience reports on either or both of the following perspectives:
Specific topics of interest include, but are not limited to the following subject categories:
This workshop accepts regular research papers within 6 pages, and short papers (new idea and position) within 4 pages.
Submitted papers must conform to the two-column IEEE conference publication format. Templates for LaTeX and Microsoft Word are available from http://www.ieee.org/conferences_events/conferences/publishing/templates.html: please use the letter format template and conference option.
Papers should be submitted in the PDF format: they must not exceed page limit. Submissions will be handled via EasyChair. Papers must neither have been previously accepted for publication nor be under submission in another conference or journal. For your paper to be published in the proceedings, at least one of the authors of the paper must register for the conference and confirm that she/he will present the paper in person. All accepted papers will be part of the ICST joint workshop proceedings published in the IEEE Digital Library.
All papers will be evaluated in terms of the following criteria:
Submissions will be handled via EasyChair: https://easychair.org/conferences/?conf=mlst2019.