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Mikael Fagerström

dblp:183/0210 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
—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 · 81% Software maintenance and evolution · 19%

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

TopicWeightPapersLastEvidence papers
Software testing › software testing evaluation
test result analysis
0.212016
Verdict machinery: on the need to automatically make sense of test results · ISSTA 2016
Software testing
automated testing
0.112016
Verdict machinery: on the need to automatically make sense of test results · ISSTA 2016
Software maintenance and evolution › release engineering
continuous integration
0.112016
Verdict machinery: on the need to automatically make sense of test results · ISSTA 2016
YearPublicationVenuePosition
2016 Verdict machinery: on the need to automatically make sense of test results
abstract
Along with technological developments and increasing competition there is a major incentive for companies to produce and market high quality products before their competitors. In order to conquer a bigger portion of the market share, companies have to ensure the quality of the product in a shorter time frame. To accomplish this task companies try to automate their test processes as much as possible. It is critical to investigate and understand the problems that occur during different stages of test automation processes. In this paper we report on a case study on automatic analysis of non-functional test results. We discuss challenges in the face of continuous integration and deployment and provide improvement suggestions based on interviews at a large company in Sweden. The key contributions of this work are filling the knowledge gap in research about performance regression test analysis automation and providing warning signs and a road map for the industry.
Mikael Fagerström, Emre Emir Ismail, Grischa Liebel, Rohit Guliani, Fredrik Larsson, Karin Nordling, Eric Knauss, Patrizio Pelliccione
ISSTA1