Mailbox Abstractions for Static Analysis of Actor Programs

Properties such as the absence of errors or bounds on mailbox sizes are hard to deduce statically for actor-based programs. This is because actor-based programs exhibit several sources of unboundedness, in addition to the non-determinism that is inherent to the concurrent execution of actors. We developed a static technique based on abstract interpretation to soundly reason in a finite amount of time about the possible executions of an actor-based program. We use our technique to statically verify the absence of errors in actor-based programs, and to compute upper bounds on the actors\u27 mailboxes. Sound abstraction of these mailboxes is crucial to the precision of any such technique. We provide several mailbox abstractions and categorize them according to the extent to which they preserve message ordering and multiplicity of messages in a mailbox. We formally prove the soundness of each mailbox abstraction, and empirically evaluate their precision and performance trade-offs on a corpus of benchmark programs. The results show that our technique can statically verify the absence of errors for more benchmark programs than the state-of-the-art analysis

Study of Code Smells: A Review and Research Agenda

Code Smells have been detected, predicted and studied by researchers from several perspectives. This literature review is conducted to understand tools and algorithms used to detect and analyze code smells to summarize research agenda. 114 studies have been selected from 2009 to 2022 to conduct this review. The studies are deeply analyzed under the categorization of machine learning and non-machine learning, which are found to be 25 and 89 respectively. The studies are analyzed to gain insight into algorithms, tools and limitations of the techniques. Long Method, Feature Envy, and Duplicate Code are reported to be the most popular smells. 38% of the studies focused their research on the enhancement of tools and methods. Random Forest and JRip algorithms are found to give the best results under machine learning techniques. We extended the previous studies on code smell detection tools, reporting a total 87 tools during the review. Java is found to be the dominant programming language during the study of smells