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Anas Shawesh

dblp:280/4696 · 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
Empirical software engineering · 67% Concurrent programming · 33%

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

TopicWeightPapersLastEvidence papers
Empirical software engineering › software fault analysis
bug study
0.412020
Actor concurrency bugs: a comprehensive study on symptoms, root causes, API usages, and differences · Proc. ACM Program. Lang. 2020
Concurrent programming
concurrency bugs
0.412020
Actor concurrency bugs: a comprehensive study on symptoms, root causes, API usages, and differences · Proc. ACM Program. Lang. 2020
Empirical software engineering
mining software repositories
0.412020
Actor concurrency bugs: a comprehensive study on symptoms, root causes, API usages, and differences · Proc. ACM Program. Lang. 2020

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

manual analysis · 0.4empirical study · 0.4
YearPublicationVenuePosition
2020 Actor concurrency bugs: a comprehensive study on symptoms, root causes, API usages, and differences
abstract
Actor concurrency is becoming increasingly important in the development of real-world software systems. Although actor concurrency may be less susceptible to some multithreaded concurrency bugs, such as low-level data races and deadlocks, it comes with its own bugs that may be different. However, the fundamental characteristics of actor concurrency bugs, including their symptoms, root causes, API usages, examples, and differences when they come from different sources are still largely unknown. Actor software development can significantly benefit from a comprehensive qualitative and quantitative understanding of these characteristics, which is the focus of this work, to foster better API documentation, development practices, testing, debugging, repairing, and verification frameworks. To conduct this study, we take the following major steps. First, we construct a set of 186 real-world Akka actor bugs from Stack Overflow and GitHub via manual analysis of 3,924 Stack Overflow questions, answers, and comments and 3,315 GitHub commits, messages, original and modified code snippets, issues, and pull requests. Second, we manually study these actor bugs and their fixes to understand and classify their symptoms, root causes, and API usages. Third, we study the differences between the commonalities and distributions of symptoms, root causes, and API usages of our Stack Overflow and GitHub actor bugs. Fourth, we discuss real-world examples of our actor bugs with these symptoms and root causes. Finally, we investigate the relation of our findings with those of previous work and discuss their implications. A few findings of our study are: (1) symptoms of our actor bugs can be classified into five categories, with Error as the most common symptom and Incorrect Exceptions as the least common, (2) root causes of our actor bugs can be classified into ten categories, with Logic as the most common root cause and Untyped Communication as the least common, (3) a small number of Akka API packages are responsible for most of API usages by our actor bugs, and (4) our Stack Overflow and GitHub actor bugs can differ significantly in commonalities and distributions of their symptoms, root causes, and API usages. While some of our findings agree with those of previous work, others sharply contrast.
Mehdi Bagherzadeh 0001, Nicholas Fireman, Anas Shawesh, Raffi Khatchadourian
Proc. ACM Program. Lang.3