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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
Times are displayed in time zone: (UTC) Coordinated Universal Time change

17:30 - 17:32
API Method Recommendation via Explicit Matching of Functionality Verb Phrases
Research Papers
Wenkai XieFudan University, China, Xin PengFudan University, China, Mingwei LiuFudan University, China, Christoph TreudeUniversity of Adelaide, Australia, Zhenchang XingAustralian National University, Australia, Xiaoxin ZhangFudan University, China, Wenyun ZhaoFudan University, China
17:33 - 17:34
Code Recommendation for Exception Handling
Research Papers
Tam NguyenAuburn University, USA, Phong VuAuburn University, USA, Tung NguyenAuburn University, USA
17:35 - 17:36
eQual: Informing Early Design Decisions
Research Papers
Arman ShahbazianGoogle, USA, Suhrid KarthikUniversity of Southern California, USA, Yuriy BrunUniversity of Massachusetts Amherst, Nenad MedvidovićUniversity of Southern California, USA
Link to publication DOI Pre-print Media Attached
17:37 - 17:38
Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matching
Research Papers
Sonal MahajanFujitsu Labs, USA, Negarsadat AbolhassaniUniversity of Southern California, USA, Mukul R. PrasadFujitsu Labs, USA
DOI Pre-print Media Attached
17:39 - 17:40
Recommender Systems: Metric Suggestion Mechanisms Applied to Adaptable Software Dashboards
Student Research Competition
Dragos StrugarInnopolis University, Russia
17:41 - 17:42
Understanding the Impact of GitHub Suggested Changes on Recommendations between Developers
Research Papers
Chris BrownNorth Carolina State University, USA, Chris ParninNorth Carolina State University, USA
17:43 - 18:00
Conversations on Recommendation
Paper Presentations
Chris BrownNorth Carolina State University, USA, Dragos StrugarInnopolis University, Russia, Mingwei LiuFudan University, China, Sonal MahajanFujitsu Labs, USA, Arman ShahbazianUniversity of Southern California, M: Massimiliano Di PentaUniversity of Sannio, Italy