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Software developers have difficulty understanding the rationale and intent behind original developers' design decisions. Code histories aim to provide richer contexts for code changes over time, but can introduce a large amount of information to the already cognitively demanding task of code comprehension. Storytelling has shown benefits in communicating complex, time-dependent information, yet programmers are reluctant to write stories for their code changes. We explored the utility of narratives made by generative AI. We conducted a within-subjects study comparing the performance of 16 programmers when recalling code history information from a list-view format versus a comparable AI-generated narrative format. Our study found that when using the story-view, participants were 16\% more successful at recalling code history information, and had 30\% less error when assessing the correctness of their responses. We did not find any significant differences in programmer's perceived mental effort or their attitudes towards reuse when using narrative code stories. 

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. 

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. 

Current programming practices rely heavily on the use of APIs (Application Programming Interfaces) and frameworks. However, APIs can be challenging to learn and use. Existing research focuses on specific barriers programmers encounter while learning APIs, providing a fragmented understanding of the process. In this paper, we analyze the holistic process of twelve programmers learning the React JS API using sensemaking theory as a guiding framework for qualitative coding of behaviors. We describe how these API learners moved through sensemaking stages and how they interacted with information during each sensemaking stage. Our results highlighted programmers’ tendency to seek understanding when they encountered problems.