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Wed 11 Nov 2020 17:35 - 17:36 at Virtual room 1 - Recommendation

When designing a software system, architects make a series of design
decisions that directly impact the system's quality. The number of available
design alternatives grows rapidly with system size, creating an enormous
space of intertwined design concerns that renders manual exploration
impractical. We present eQual, a model-driven technique for simulation-based
assessment of architectural designs. While it is not possible to guarantee
optimal decisions so early in the design process, eQual improves decision
quality. eQual is effective in practice because it (1) limits the amount of
information the architects have to provide and (2) adapts optimization
algorithms to effectively explore massive spaces of design alternatives. We
empirically demonstrate that eQual yields designs whose quality is comparable
to a set of systems' known optimal designs. A user study shows that, compared
to the state-of-the-art, engineers using eQual produce statistically
significantly higher-quality designs with a large effect size, are
statistically significantly more confident in their designs, and find eQual
easier to use.

Wed 11 Nov

Displayed time zone: (UTC) Coordinated Universal Time change

17:30 - 18:00
17:30
2m
Talk
API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Research Papers
Wenkai Xie Fudan University, China, Xin Peng Fudan University, China, Mingwei Liu Fudan University, China, Christoph Treude University of Adelaide, Australia, Zhenchang Xing Australian National University, Australia, Xiaoxin Zhang Fudan University, China, Wenyun Zhao Fudan University, China
DOI
17:33
1m
Talk
Code Recommendation for Exception Handling
Research Papers
Tam Nguyen Auburn University, USA, Phong Vu Auburn University, USA, Tung Nguyen Auburn University, USA
DOI
17:35
1m
Talk
eQual: Informing Early Design Decisions
Research Papers
Arman Shahbazian Google, USA, Suhrid Karthik University of Southern California, USA, Yuriy Brun University of Massachusetts Amherst, Nenad Medvidović University of Southern California, USA
Link to publication DOI Pre-print Media Attached
17:37
1m
Talk
Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching
Research Papers
Sonal Mahajan Fujitsu Labs, USA, Negarsadat Abolhassani University of Southern California, USA, Mukul Prasad Fujitsu Labs, USA
DOI Pre-print Media Attached
17:39
1m
Talk
Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards
Student Research Competition
Dragos Strugar Innopolis University, Russia
DOI
17:41
1m
Talk
Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
Research Papers
Chris Brown North Carolina State University, USA, Chris Parnin North Carolina State University, USA
DOI
17:43
17m
Talk
Conversations on Recommendation
Paper Presentations
Chris Brown North Carolina State University, USA, Dragos Strugar Innopolis University, Russia, Mingwei Liu Fudan University, China, Sonal Mahajan Fujitsu Labs, USA, Arman Shahbazian University of Southern California, M: Massimiliano Di Penta University of Sannio, Italy