Ali M. Al-Yami

dblp:64/2974 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2

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
2 papers
Software testing · 94% Program verification · 6%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
test input generation
0.021998
Automated Regression Test Generation · ISSTA 1998
Assertion-Oriented Automated Test Data Generation · ICSE 1996
Software testing
regression testing
0.011998
Automated Regression Test Generation · ISSTA 1998
Software testing › test generation
white-box test generation
0.011998
Automated Regression Test Generation · ISSTA 1998
Program verification › dynamic verification › runtime verification
assertion checking
0.011996
Assertion-Oriented Automated Test Data Generation · ICSE 1996

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

test data generation · 0.0differential execution · 0.0
YearPublicationVenuePosition
1998 Automated Regression Test Generation
abstract
Regression testing involves testing the modified program in order to establish the confidence in the modifications. Existing regression testing methods generate test cases to satisfy selected testing criteria in the hope that this process may reveal faults in the modified program. In this paper we present a novel approach of automated regression test generation in which all generated test cases uncover an error(s). This approach is used to test the common functionality of the original program and its modified version, i.e., it is used for programs whose functionality is unchanged after modifications. The goal in this approach is to identify test cases for which the original program and the modified program produce different outputs. If such a test is found, then this test uncovers an error. The problem of finding such a test case may be reduced to the problem of finding program input on which a selected statement is executed. As a result, existing methods of automated test data generation for white-box testing may be used to generate these tests. Our experiments have shown that our approach may improve the chances of finding software errors as compared to the existing methods of regression testing. The advantage of our approach is that it is fully automated and that all generated test cases reveal an error(s).
Bogdan Korel, Ali M. Al-Yami
ISSTA2
1996 Assertion-Oriented Automated Test Data Generation
Bogdan Korel, Ali M. Al-Yami
ICSE2