Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams

Khatchadourian, Raffi; Tang, Yiming; Bagherzadeh, Mehdi; Ahmed, Syed Naeem

doi:10.1109/icse.2019.00072

Cited by 23 publications

(23 citation statements)

References 54 publications

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“…For example, many server applications deal with thread pools that span the JVM, and developers may be leery of the interactions of such pools with the underlying stream parallelization run time system. We found this to be the case with several pull requests [15,45] that were issued by Khatchadourian et al [24] as part of their refactoring evaluation to introduce parallel streams into existing projects. It may also be the case that the locations where streams operate are already fast enough or do not process significant amounts of data [7,30].…”

Section: Discussionmentioning

confidence: 90%

“…Many streams are ordered (86.42%), which can prevent optimal performance of parallel streams under certain conditions [24,35,40]. Thus, even if streams were run in parallel, they may not reap all of the benefits.…”

Section: Discussionmentioning

confidence: 99%

“…All the subjects have their sources publicly available on GitHub and include popular libraries, frameworks, and applications. Many subjects were selected from previous studies [20,21,22,24], others because they contained relatively diverse stream operations and exhibited non-trivial metrics, including stars, forks, and number of collaborators. It was necessary to use different subjects for different parts of the study due to the computationally intensive nature of some of the experiments.…”

Section: Study Subjectsmentioning

confidence: 99%

“…7]. This requires determining whether running stream code in parallel yields an efficient yet interference-free program [24] and ensuring that no operations on different threads interleave [42].…”

Section: Introductionmentioning

confidence: 99%

“…We analyzed 34 Java projects and 5.53 million lines of source code (SLOC), along with 140,446 code patches (git commits), of which 719 were manually Listing 1 Snippet of Widget collection processing using Java 8 streams [24,39]. Set<Double> heavyWidgetWeightSet = orderedWidgets.parallelStream().map(Widget::getWeight)…”

Section: Introductionmentioning

confidence: 99%

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

Khatchadourian

Tang

Bagherzadeh

et al. 2020

Lecture Notes in Computer Science

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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.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Study Subjectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Khatchadourian

Tang

Bagherzadeh

et al. 2020

Lecture Notes in Computer Science

Self Cite

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Large‐scale characterization of Java streams

et al. 2023

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Java streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators.

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