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Duo Men

dblp:269/8174 · DBLP profile ↗
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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
Software testing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › test optimization
test recommendation
0.412020
Test recommendation system based on slicing coverage filtering · ISSTA 2020
Software testing
unit testing
0.412020
Test recommendation system based on slicing coverage filtering · ISSTA 2020

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

program slicing · 0.4
YearPublicationVenuePosition
2020 Test recommendation system based on slicing coverage filtering
abstract
Software testing plays a crucial role in software lifecycle. As a basic approach of software testing, unit testing is one of the necessary skills for software practitioners. Since testers are required to understand the inner code of the software under test(SUT) while writing a test case, testers usually need to learn how to detect the bug within SUT effectively. When novice programmers started to learn writing unit tests, they will generally watch a video lesson or reading unit tests written by others. These learning approaches are either time-consuming or too hard for a novice. To solve these problems, we developed a system, named TeSRS, to assist novice programmers to learn unit testing. TeSRS is a test recommendation system which can effectively assist test novice in learning unit testing. Utilizing program slice technique, TeSRS has gotten an enormous amount of test snippets from superior crowdsourcing test scripts. Depending on these test snippets, TeSRS provides novices a easier way for unit test learning. To sum up, TeSRS can help test novices (1) obtain high level design ideas of unit test case and (2) improve capabilities(e.g. branch coverage rate and mutation coverage rate) of their test scripts. TeSRS has built a scalable corpus composed of over 8000 test snippets from more than 25 test problems. Its stable performance shows effectiveness in unit test learning.
Ruixiang Qian, Yuan Zhao 0010, Duo Men, Yang Feng 0003, Qingkai Shi, Zhenyu Chen 0001
ISSTA3