[Engineering Paper] A Tool for Optimizing Java 8 Stream Software via Automated Refactoring

Lecture Notes in Computer Science

et al. 2020

Self Cite

Streaming APIs allow for big data processing of native data structures by providing MapReduce-like operations over these structures. However, unlike traditional big data systems, these data structures typically reside in shared memory accessed by multiple cores. Although popular, this emerging hybrid paradigm opens the door to possibly detrimental behavior, such as thread contention and bugs related to non-execution and non-determinism. This study explores the use and misuse of a popular streaming API, namely, Java 8 Streams. The focus is on how developers decide whether or not to run these operations sequentially or in parallel and bugs both specific and tangential to this paradigm. Our study involved analyzing 34 Java projects and 5.53 million lines of code, along with 719 manually examined code patches. Various automated, including interprocedural static analysis, and manual methodologies were employed. The results indicate that streams are pervasive, parallelization is not widely used, and performance is a crosscutting concern that accounted for the majority of fixes. We also present coincidences that both confirm and contradict the results of related studies. The study advances our understanding of streams, as well as benefits practitioners, programming language and API designers, tool developers, and educators alike.

Section: Methodsmentioning

confidence: 99%

An Empirical Study on the Use and Misuse of Java 8 Streams

Khatchadourian

Tang

Lecture Notes in Computer Science

et al. 2020

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“…The first author is a Software Engineer and Programming Languages professor with extensive expertise in both actor and multithreaded concurrent systems Rajan 2015, 2017;Khatchadourian et al 2018;Long et al 2016]. The second and third authors are Ph.D. students with extensive coursework in actor, multithreaded and cloud systems.…”

Section: Data Collectionmentioning

confidence: 99%

Actor concurrency bugs: a comprehensive study on symptoms, root causes, API usages, and differences

Proc. ACM Program. Lang.

Fireman

Shawesh

et al. 2020

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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 lowlevel 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: ❶ 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, ❷ 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, ❸ a small number of Akka API packages are responsible for most of API usages by our actor bugs, and ❹ 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. CCS Concepts: • Theory of computation → Concurrency; • General and reference → Empirical studies.

“…Our approach was implemented as a publicly available, open source Eclipse IDE [31] plug-in [28] and built upon WALA [32] and SAFE [33]. Eclipse is leveraged for its extensive refactoring support [40] and that it is completely open-source for all Java development.…”

Section: Evaluation a Implementationmentioning

confidence: 99%

“…Some of the refactoring failures were due to cases currently not handled by our tool (F5), which are rooted in implemen- tation details related to model differences between representations [28]. For example, streams declared inside inner (embedded) classes are problematic as such classes are part of the outer AST but the instruction-based IR is located elsewhere.…”

Section: B Experimental Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams

Khatchadourian

Tang

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

et al. 2019

Self Cite

Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semanticspreserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ∼642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.