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Chenqian Zhu

dblp:282/6661 · DBLP profile ↗
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1ranked-venue papers
1as 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 · 1 first-author

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
Software testing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › test optimization
test recommendation
0.412020
HomoTR: Online Test Recommendation System Based on Homologous Code Matching · ASE 2020
Software testing
test reuse
0.112020
HomoTR: Online Test Recommendation System Based on Homologous Code Matching · ASE 2020
Software testing › test generation
unit test generation
0.112020
HomoTR: Online Test Recommendation System Based on Homologous Code Matching · ASE 2020

Methods — techniques the papers use, named apart from their topics

code homology measurement · 0.4
YearPublicationVenuePosition
2020 HomoTR: Online Test Recommendation System Based on Homologous Code Matching
abstract
A growing number of new technologies are used in test development. Among them, automatic test generation, a promising technology to improve the efficiency of unit testing, currently performs not satisfactory in practice. Test recommendation, like code recommendation, is another feasible technology for supporting efficient unit testing and gets increasing attention. In this paper, we develop a novel system, namely HomoTR, which implements online test recommendations by measuring the homology of two methods. If the new method under test shares homology with an existing method that has test cases, HomoTR will recommend the test cases to the new method. The preliminary experiments show that HomoTR can quickly and effectively recommend test cases to help the developers improve the testing efficiency. Besides, HomoTR has been integrated into the MoocTest platform successfully, so it can also execute the recommended test cases automatically and visualize the testing results (e.g., Branch Coverage) friendly to help developers understand the process of testing. The demo video of HomoTR can be found at https://youtu.be/_227EfcUbus.
Chenqian Zhu, Weisong Sun, Qin Liu 0002, Yangyang Yuan, Chunrong Fang
ASE1