Mohsen Moradi-Moghadam

dblp:173/6815 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0004-3215-0078ORCID · reported

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

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

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 · 56% Concurrent programming · 44%

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

TopicWeightPapersLastEvidence papers
Concurrent programming
concurrency bug detection
0.712023
µAkka: Mutation Testing for Actor Concurrency in Akka using Real-World Bugs · ESEC/SIGSOFT FSE 2023
Software testing
mutation testing
0.712023
µAkka: Mutation Testing for Actor Concurrency in Akka using Real-World Bugs · ESEC/SIGSOFT FSE 2023
Software testing › test suite evaluation
test effectiveness evaluation
0.212023
µAkka: Mutation Testing for Actor Concurrency in Akka using Real-World Bugs · ESEC/SIGSOFT FSE 2023

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

mutation operators · 0.7PIT · 0.7
YearPublicationVenuePosition
2023 µAkka: Mutation Testing for Actor Concurrency in Akka using Real-World Bugs
abstract
Actor concurrency is becoming increasingly important in the real world and mission-critical software. This requires these applications to be free from actor bugs, that occur in the real world, and have tests that are effective in finding these bugs. Mutation testing is a well-established technique that transforms an application to induce its likely bugs and evaluate the effectiveness of its tests in finding these bugs. Mutation testing is available for a broad spectrum of applications and their bugs, ranging from web to mobile to machine learning, and is used at scale in companies like Google and Facebook. However, there still is no mutation testing for actor concurrency that uses real-world actor bugs. In this paper, we propose 𝜇Akka, a framework for mutation testing of Akka actor concurrency using real actor bugs. Akka is a popular industrial-strength implementation of actor concurrency. To design, implement, and evaluate 𝜇Akka, we take the following major steps: (1) manually analyze a recent set of 186 real Akka bugs from Stack Overflow and GitHub to understand their causes; (2) design a set of 32 mutation operators, with 138 source code changes in Akka API, to emulate these causes and induce their bugs; (3) implement these operators in an Eclipse plugin for Java Akka; (4) use the plugin to generate 11.7k mutants of 10 real GitHub applications, with 446.4k lines of code and 7.9k tests; (5) run these tests on these mutants to measure the quality of mutants and effectiveness of tests; (6) use PIT to generate 26.2k mutants to compare 𝜇Akka and PIT mutant quality and test effectiveness. PIT is a popular mutation testing tool with traditional operators; (7) manually analyze the bug coverage and overlap of 𝜇Akka, PIT, and actor operators in a previous work; and (8) discuss a few implications of our findings. Among others, we find that 𝜇Akka mutants are higher quality, cover more bugs, and tests are less effective in detecting them.
Mohsen Moradi-Moghadam, Mehdi Bagherzadeh 0001, Raffi Khatchadourian, Hamid Bagheri
ESEC/SIGSOFT FSE1