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Background: Software development relies on collaborative problem-solving. Understanding previously addressed problems in software is crucial for developers to identify and repurpose functionalities for new problem-solving contexts. Objective: We explore the barriers programmers encounter during code repurposing and investigate how access to historical context about the original developer's goals may affect this process. Method: We present an exploratory study of 16 programmers who completed two code repurposing tasks in different code bases. Participants completed these tasks both with and without access to the historical information of the original developer's goals. We explore how programmers use analogical reasoning to identify and apply existing software artifacts to new goals. Results: We show that programmers often failed to notice analogies, made false analogies, and underestimated the value of reuse. Even when useful analogies were made, programmers struggled to find the relevant code. We also describe the patterns of how participants utilized code histories. Conclusion: We highlight the barriers programmers face in noticing and applying analogies during code reuse. We suggest design recommendations for future tools to allow lightweight evaluation of code to help programmers identify reuse opportunities.
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React example viability for efficient API learning (REVEAL): A tool to help programmers utilize incompatible code examples in React. js
Programmers typically learn APIs on-the-fly through web examples. Incompatibilities and omissions in copied example code can create barriers for these learners. We present an analysis of example usage barriers programmers faced in a previous study of React.js novices. We show that a small set of errors prevented programmers from using most found code examples. In response, we built REVEAL to detect and repair the common errors we identified in copied code. We describe the formative evaluation of REVEAL and show that REVEAL users were more likely to successfully integrate code examples than participants in the previous study.
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- Award ID(s):
- 2128128
- PAR ID:
- 10467289
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Journal of Computer Languages
- Volume:
- 75
- ISSN:
- 2590-1184
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.cola.2023.101201
