Debugging data flows in reactive programs

Banken, Herman; Meijer, Erik; Gousios, Georgios

doi:10.1145/3180155.3180156

Cited by 24 publications

(17 citation statements)

References 36 publications

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“…In this paper, we follow a three-phase sequential exploratory strategy [3]- [5] to study the challenges that developers encounter when debugging and developing Spark applications. First, we conduct a qualitative study on 1,000 Spark-related questions on Stack Overflow, which reaches a 95% confidence level and 3% confidence interval.…”

Section: Introductionmentioning

confidence: 99%

Going big: a large-scale study on what big data developers ask

Bagherzadeh

Khatchadourian

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

129

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Software developers are increasingly required to write big data code. However, they find big data software development challenging. To help these developers it is necessary to understand big data topics that they are interested in and the difficulty of finding answers for questions in these topics. In this work, we conduct a large-scale study on Stackoverflow to understand the interest and difficulties of big data developers. To conduct the study, we develop a set of big data tags to extract big data posts from Stackoverflow; use topic modeling to group these posts into big data topics; group similar topics into categories to construct a topic hierarchy; analyze popularity and difficulty of topics and their correlations; and discuss implications of our findings for practice, research and education of big data software development and investigate their coincidence with the findings of previous work. CCS CONCEPTS • General and reference → Empirical studies; • Theory of computation → Concurrency.

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Section: Introductionmentioning

confidence: 99%

Going big: a large-scale study on what big data developers ask

Bagherzadeh

Khatchadourian

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

129

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“…Recently, Ramson and Hirschfeld [2017] proposed active expressions as a primitive for change detection, upon which different kinds of RP can be built. Other recent research directions in RP include debugging [Banken et al 2018;Perez and Nilsson 2017;Salvaneschi and Mezini 2016], thread-safe concurrency [Drechsler et al 2018], and application to new domains such as IoT and edge computing [Calus et al 2017] and autonomous vehicles [Finkbeiner 129:27 et al 2017]. In the distributed setting, the DREAM reactive middleware Salvaneschi 2014, 2018] allows selecting different levels of consistency guarantees for distributed RP.…”

Section: Performancementioning

confidence: 99%

Distributed system development with ScalaLoci

Weisenburger

Köhler

Salvaneschi

2018

Proc. ACM Program. Lang.

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Distributed applications are traditionally developed as separate modules, often in different languages, which react to events, like user input, and in turn produce new events for the other modules. Separation into components requires time-consuming integration. Manual implementation of communication forces programmers to deal with low-level details. The combination of the two results in obscure distributed data flows scattered among multiple modules, hindering reasoning about the system as a whole. The ScalaLoci distributed programming language addresses these issues with a coherent model based on placement types that enables reasoning about distributed data flows, supporting multiple software architectures via dedicated language features and abstracting over low-level communication details and data conversions. As we show, ScalaLoci simplifies developing distributed systems, reduces error-prone communication code and favors early detection of bugs. CCS Concepts: • Software and its engineering → Distributed programming languages; Domain specific languages; • Theory of computation → Distributed computing models;

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“…Ramson and Hirschfeld recently proposed Active Expressions [50] as a fundamental primitive to implement different flavours of RP. Other current RP research directions include debugging [51], [52], [53] and application to new domains such as IoT/edge computing and autonomous vehicles [54], [55], [56].…”

Section: Related Workmentioning

confidence: 99%

On the Semantics of Distributed Reactive Programming: The Cost of Consistency

Margara

Salvaneschi

2018

IIEEE Trans. Software Eng.

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The reactive programming paradigm aims to simplify the development of reactive systems. It provides abstractions to define time-changing values that are automatically updated by the runtime according to their dependencies. The benefits of reactive programming in distributed settings have been recognized for long. Yet, existing solutions for distributed reactive programming enforce the same semantics as in single processes, introducing communication and synchronization costs that hamper scalability. Establishing suitable abstractions for distributed reactive programming demands for a deeper investigation of the semantics of change propagation. This paper takes a foundational approach and defines precise propagation semantics in terms of consistency guarantees that constrain the order and isolation of value updates. We study the benefits and costs of these consistency guarantees both theoretically and empirically, using case studies and synthetic benchmarks. We show that different applications require different levels of consistency and that manually implementing the required level on a middleware that provides a lower one annuls the abstraction improvements of reactive programming. This motivates a framework that enables the developers to select the best trade-off between consistency and overhead for the problem at hand. To this end, we present DREAM, a distributed reactive programming middleware with flexible consistency guarantees.

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Debugging data flows in reactive programs

Cited by 24 publications

References 36 publications

Going big: a large-scale study on what big data developers ask

Going big: a large-scale study on what big data developers ask

Distributed system development with ScalaLoci

On the Semantics of Distributed Reactive Programming: The Cost of Consistency

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