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

Khatchadourian, Raffi; Tang, Yiming; Bagherzadeh, Mehdi; Ray, Baishakhi

doi:10.1007/978-3-030-45234-6_5

Cited by 16 publications

(19 citation statements)

References 32 publications

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“…The second and third authors are Ph.D. students with extensive coursework in actor, multithreaded and cloud systems. The fourth author is a Software Engineer professor with extensive expertise in parallel and streaming systems [Khatchadourian et al , 2020. All authors have several years of industrial work experience.…”

Section: Data Collectionmentioning

confidence: 99%

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

Bagherzadeh

Fireman

Shawesh

et al. 2020

Proc. ACM Program. Lang.

Self Cite

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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.

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Section: Data Collectionmentioning

confidence: 99%

“…Wang et al [2017] study 57 concurrency bugs in 53 open-source Node.js software and classify their root causes, patterns, impacts, manifestation, and fix strategies. Khatchadourian et al [2020] also report on API misuse for Java streams.…”

Section: Related Workmentioning

confidence: 99%

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

Bagherzadeh

Fireman

Shawesh

et al. 2020

Proc. ACM Program. Lang.

Self Cite

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“…(In 28 randomly selected open source Java projects from the RepoReapers dataset [Munaiah et al 2017] that we could build we found 6 879 sequential stream pipelines and 49 parallel stream pipelines. A recent study by Khatchadourian et al [2020b] confirms this finding.) Still, sequential stream pipelines are often used in multi-threaded applications, so we cannot assume a single-threaded execution environment when designing optimization techniques.…”

Section: Background: Pull-and Push-style Stream Apismentioning

confidence: 53%

Eliminating abstraction overhead of Java stream pipelines using ahead-of-time program optimization

Møller

2020

Proc. ACM Program. Lang.

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Java 8 introduced streams that allow developers to work with collections of data using functional-style operations. Streams are often used in pipelines of operations for processing the data elements, which leads to concise and elegant program code. However, the declarative data processing style comes at a cost. Compared to processing the data with traditional imperative language mechanisms, constructing stream pipelines requires extra heap objects and virtual method calls, which often results in significant run-time overheads. In this work we investigate how to mitigate these overheads to enable processing data in the declarative style without sacrificing performance. We argue that ahead-of-time bytecode-to-bytecode transformation is a suitable approach to optimization of stream pipelines, and we present a static analysis that is designed to guide such transformations. Experimental results show a significant performance gain, and that the technique works for realistic stream pipelines. For 10 of 11 micro-benchmarks, the optimizer is able to produce bytecode that is as effective as hand-written imperative-style code. Additionally, 77% of 6879 stream pipelines found in real-world Java programs are optimized successfully.

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“…We believe that as a research community, we do not understand student conceptions and misconceptions of hofs very well at all. It would also be interesting to relate findings at this level to those on a larger scale, such as Khatchadourian, et al 's analysis of Java streams [19].…”

Section: Reflection On Ourmentioning

confidence: 99%

Developing Behavioral Concepts of Higher-Order Functions

Krishnamurthi

Fisler

2021

Proceedings of the 17th ACM Conference on International Computing Education Research

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Motivation. Higher-order functions are a standard and increasingly central component in many kinds of modern programming, including data science and Web development. Yet little research has been devoted to student learning or understanding of this topic.Objectives. We conducted formative research on how well students are able to correlate higher-order functions with their inputoutput behavior. We also wanted to evaluate a variety of techniques for assessing their understanding.Method. We created a series of instruments in which students were given either concrete input/output examples or abstracted diagrams of list transformations. Students were asked to cluster or classify these examples by their behavior, sometimes against a concrete list of higher-order functions and sometimes free-form. We administered these over the course of a month, and then once again three months later.Results. We find that students initially have several difficulties with clustering higher-order function examples. With different instruments, we find that students are later able to do quite well, largely avoiding large-scale errors but making several small-scale ones. We also find some evidence of growth in their thinking about these operations. We also find weaknesses in the nature and order of techniques we used.Discussion. Higher-order functions deserve far more attention than they have been paid in the literature on programming education. Their increasing use in several important domains makes this need critical. Our proposed methods for conducting such research are another contribution of this work. Our findings and methods should also be relevant for exploring how students understand libraries and APIs.

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

Cited by 16 publications

References 32 publications

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

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

Eliminating abstraction overhead of Java stream pipelines using ahead-of-time program optimization

Developing Behavioral Concepts of Higher-Order Functions

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