How should static analysis tools explain anomalies to developers?

Barik, Titus

doi:10.1145/2950290.2983968

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Another problem that static analysis tools often face is that they operate more or less like black boxes. They receive code pieces as input, perform internal operations that are not always fully comprehensible for the developer, and give results that could be better understood if some of the information from the analysis' internal workings would be made accessible to the developer [3]. And even if not extracted from the analysis process itself, heuristic approaches that try to reason about the analysis output after the fact could lead to more comprehensive explanations of the underlying problem.…”

Section: Hovering Over the Function Name Also Shows The Exact Cost Without Having To Open The Detail Viewmentioning

confidence: 99%

Interactive Static Software Performance Analysis in the IDE

Beigelbeck

Aniche

Cito

2021

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)

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Detecting performance issues due to suboptimal code during the development process can be a daunting task, especially when it comes to localizing them after noticing performance degradation after deployment. Static analysis has the potential to provide early feedback on performance problems to developers without having to run profilers with expensive (and often unavailable) performance tests. We develop a VSCode tool that integrates the static performance analysis results from Infer via code annotations and decorations (surfacing complexity analysis results in context) and side panel views showing details and overviews (enabling explainability of the results). Additionally, we design our system for interactivity to allow for more responsiveness to code changes as they happen. We evaluate the efficacy of our tool by measuring the overhead that the static performance analysis integration introduces in the development workflow. Further, we report on a case study that illustrates how our system can be used to reason about software performance in the context of a real performance bug in the ElasticSearch open-source project.

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Section: Hovering Over the Function Name Also Shows The Exact Cost Without Having To Open The Detail Viewmentioning

confidence: 99%

Interactive Static Software Performance Analysis in the IDE

Beigelbeck

Aniche

Cito

2021

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)

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“…We grouped the remaining articles (23 articles, 50%) into studies that focus on the programming environments used by professional programmers. The articles show the following types of improvements for programming environments: (i) related to error comprehension [48]- [51]; (ii) in the visualization of codes [52]- [55]; (iii) in the ease of code refactoring [56], [57]; and (iv) the ease of making code changes and maintaining control over changes [58]- [61].…”

Section: B: Programmer Environment For Programmersmentioning

confidence: 99%

Programmer eXperience: A Systematic Literature Review

et al. 2019

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Programmers use various software development artifacts in their work, such as programming environments, design documents, and programming codes. These software artifacts can be studied and improved based on usability and User eXperience (UX) factors. In this paper, we consider programmers to be a specific case of users and analyze different elements that influence their experience in this specific context. We conducted a systematic literature review of papers published over the last ten years related to 1) the definition of the Programmer eXperience (PX); 2) the PX, UX, and usability factors regarding the programming environments, design documents, and programming codes; and 3) sets of heuristics to evaluate the software development artifacts mentioned before. We analyzed 73 articles, and the results obtained show that: 1) the important elements that influence the PX are the motivation of programmers and the choice of tools they use in their work, such as programming environments; 2) most of the identified studies (59%) aimed to evaluate the influence of the PX, UX, and usability on programming environments; 3) the majority of the studies (70%) used methods such as usability tests and/or heuristic evaluation methods; and 4) four sets of heuristics are used to evaluate software development artifacts in relation to programming environments, programming languages, and application programming interfaces. The results suggest that further research in this area is necessary to better understand and evaluate the concept of the PX.

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“…Pettit and Gee report in their paper that by simply making more useful and customized error messages for certain common programmer mistakes will not make students less likely to repeat the same errors [6], but their results are not conclusive. In a recent paper on static analysis tools, Barik argues that these tools should explain the steps they performed [7]. A study with eye tracking supports that understanding error messages is vital for the success of a programming task and as difficult as reading source code [8].…”

Section: Related Workmentioning

confidence: 99%

Investigating Compilation Errors of Students Learning Haskell

Németh

Choi

Makihara

et al. 2019

Electron. Proc. Theor. Comput. Sci.

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While functional programming is an efficient way to express complex software, functional programming languages have a steep learning curve. Haskell can be challenging to learn for students who were only introduced to imperative programming. It is important to look for methods and tools that may reduce the difficulty of learning functional programming. Finding methods to help students requires understanding the errors that students make while learning Haskell.There are several previous studies revealing data about Haskell compiler errors, but they do not focus on the analysis of the compiler errors or they only study a certain kind of compiler errors.This study investigates compilation errors of novice Haskell students and make suggestions on how their learning efficiency can be improved. Unlike previous studies we focus on uncovering the root problems with the student solutions by analysing samples of their submissions.

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How should static analysis tools explain anomalies to developers?

Cited by 11 publications

References 22 publications

Interactive Static Software Performance Analysis in the IDE

Interactive Static Software Performance Analysis in the IDE

Programmer eXperience: A Systematic Literature Review

Investigating Compilation Errors of Students Learning Haskell

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