Demo Data from EDBT 2026 / reviewers in the wild / expert
Shivani Doshi
dblp:280/5558
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Program synthesis and code generation · 50% Empirical software engineering · 50% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › mining software repositories › source-code mining
API mining |
0.4 | 1 | 2020 | Digging for fold: synthesis-aided API discovery for Haskell · Proc. ACM Program. Lang. 2020 |
Program synthesis and code generation
type-directed synthesis |
0.4 | 1 | 2020 | Digging for fold: synthesis-aided API discovery for Haskell · Proc. ACM Program. Lang. 2020 |
Methods — techniques the papers use, named apart from their topics
type inference · 0.4program synthesis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Digging for fold: synthesis-aided API discovery for HaskellabstractWe present Hoogle+, a web-based API discovery tool for Haskell. A Hoogle+ user can specify a programming task using either a type, a set of input-output tests, or both. Given a specification, the tool returns a list of matching programs composed from functions in popular Haskell libraries, and annotated with automatically-generated examples of their behavior. These features of Hoogle+ are powered by three novel techniques. First, to enable efficient type-directed synthesis from tests only, we develop an algorithm that infers likely type specifications from tests . Second, to return high-quality programs even with ambiguous specifications, we develop a technique that automatically eliminates meaningless and repetitive synthesis results . Finally, we show how to extend this elimination technique to automatically generate informative inputs that can be used to demonstrate program behavior to the user. To evaluate the effectiveness of Hoogle+ compared with traditional API search techniques, we perform a user study with 30 participants of varying Haskell proficiency. The study shows that programmers equipped with Hoogle+ generally solve tasks faster and were able to solve 50% more tasks overall. Michael James 0003, Zheng Guo 0003, Ziteng Wang 0001, Shivani Doshi, Hila Peleg, Ranjit Jhala, Nadia Polikarpova |
Proc. ACM Program. Lang. | 4 |