Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Thu 14 Nov 2019 17:20 - 17:30 at Hillcrest - Software Development Chair(s): Hitesh Sajnani

More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SSFs) which gradually learn and adapt to the users’ preferences. A key property of SSFs is their ability to learn based on data resulting from the interaction with the user (implicit and explicit feedback)—which we call trainability. Newly developed and enhanced features in a SSF must be evaluated based on their effect on the trainability of the system. Despite recent approaches for continuous deployment of machine learning systems, trainability evaluation is not yet part of continuous integration and deployment (CID) pipelines. In this paper, we describe the different facets of trainability for the development of SSFs. We also present our approach for automated trainability evaluation within an automotive CID framework which proposes to use automated quality gates for the continuous evaluation of machine learning models. The results from our indicative evaluation based on real data from eight BMW cars highlight the importance of continuous and rigorous trainability evaluation in the development of SSFs.

Thu 14 Nov

16:00 - 17:40: Papers - Software Development at Hillcrest
Chair(s): Hitesh SajnaniMicrosoft
ase-2019-Journal-First-Presentations16:00 - 16:20
What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)
Amritanshu AgrawalWayfair, Wei FuDepartment of Computer Science, North Carolina State University, Tim MenziesNorth Carolina State University
Link to publication
ase-2019-papers16:20 - 16:40
Cautious Adaptation of Defiant Components
Paulo MaiaState University of Ceará, Lucas VieiraState University of Ceará, Matheus ChagasState University of Ceará, Yijun YuThe Open University, UK, Andrea ZismanThe Open University, Bashar NuseibehThe Open University (UK) & Lero (Ireland)
ase-2019-Industry-Showcase16:40 - 17:00
Better Development of Safety Critical Systems:Chinese High Speed Railway System Development Experience Report
Zhiwei WuEast China Normal University, Jing LiuEast China Normal University, Xiang ChenCASCO Signal Ltd.
ase-2019-papers17:00 - 17:20
Active Hotspot: An Issue-Oriented Model to Monitor Software Evolution and Degradation
Qiong FengDrexel University, Yuanfang Cai Drexel University, Rick KazmanUniversity of Hawai‘i at Mānoa, Di CuiXi'an Jiaotong University, Ting LiuXi'an Jiaotong University, Hongzhou FangDrexel University
ase-2019-papers17:20 - 17:30
Automated Trainability Evaluation for Smart Software Functions
Ilias GerostathopoulosTechnical University of Munich, Stefan KugeleTechnical University of Munich, Christoph SeglerBMW Group Research, New Technologies, Innovations, Tomas BuresCharles University, Czech Republic, Alois KnollTechnical University of Munich
ase-2019-Demonstrations17:30 - 17:40
Lancer: Your Code Tell Me What You Need
Shufan ZhouSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Beijun ShenSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Hao ZhongShanghai Jiao Tong University