Characterizing Java Streams in the Wild

Romera, Eduardo; Rosà, Andrea; Basso, Matteo; Villazón, Alex; Orellana, Adriana; Zenteno, Angel; Rivero, Jhon; Binder, Walter

doi:10.1109/iceccs54210.2022.00025

Cited by 2 publications

(7 citation statements)

References 17 publications

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“…This work significantly extends our previous work on characterizing Java streams at a large scale 29 . We improved our methodology enabling large‐scale characterization of stream processing (Section 4).…”

Section: Introductionmentioning

confidence: 69%

“…This is a key difference w.r.t. our previous study, 29 which analyzed a software project, regardless of its popularity. Considering projects with at least three stars enables us to better discard too small or toy projects.…”

Section: Characterizing Streamsmentioning

confidence: 99%

“…This is an important difference w.r.t. our previous study, 29 which reported the use of streams regardless of their origin, including the analysis of streams used by test‐harness classes, the build system, JVM‐related processes, and streams executed internally by the Java class library.…”

Section: Characterizing Streamsmentioning

confidence: 99%

“…We use Stream-Analyzer to profile all streams used during the execution of JUnit tests available in a software project, if any. Prior work 20,29,48 succeed to analyze multiple runtime characteristics of workloads exercised by unit tests, providing relevant statistics and revealing code patterns. Following this approach, we leverage JUnit tests to automatically exercise code available in publicly available software projects, enabling the otherwise extremely tedious task of locating candidate executable workloads for the massive application of dynamic analysis.…”

Section: Project Analysismentioning

confidence: 99%

“…This work significantly extends our previous work on characterizing Java streams at a large scale. 29 We improved our methodology enabling large-scale characterization of stream processing (Section 4). Our new methodology significantly extends the scope of our study, that is, we consider new runtime metrics, we consider a much larger time frame, use the latest long-term support Java version, and safely increase the analysis timeout to target long-running code.…”

mentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 69%

Section: Characterizing Streamsmentioning

confidence: 99%

Section: Characterizing Streamsmentioning

confidence: 99%

Section: Project Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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