Scholars have investigated numerous barriers to accessible software development tools and processes for Blind and Low Vision (BLV) developers. However, the research community has yet to study the accessibility of software development meetings, which are known to play a crucial role in software development practice. We conducted semi-structured interviews with 26 BLV software professionals about software development meeting accessibility. We found four key themes related to in-person and remote software development meetings: (1) participants observed that certain meeting activities and software tools used in meetings were inaccessible, (2) participants performed additional labor in order to make meetings accessible, (3) participants avoided disclosing their disability during meetings due to fear of career repercussions, (4) participants suggested technical, social and organizational solutions for accessible meetings, including developing their own solutions. We suggest recommendations and design implications for future accessible software development meetings including technical and policy-driven solutions.
https://doi.org/10.1145/3613904.3642130
Programmers regularly explore the execution of code examples to verify assumptions by adding print statements or commenting in and out setup code in their implementation to isolate code paths of interest. In our formative study on developing embedded programs, where proximity to hardware dictates low abstraction levels, we observed that wrong assumptions occur frequently. However, traditional editors for embedded programs lack support for such explorations. Consequently, programmers have to re-create and clean up setup and print statements in their code for each example. MµSE supports isolated explorations of code examples by promoting examples to first-class entities that allow for the mocking of side effects from code and hardware, which could interfere with examples, and automatically showing values of expressions, replacing print statements for debugging. Our exploratory study found that MµSE supports participants in developing an understanding of software and hardware components and identifying false assumptions from observation of incorrect behavior.
https://doi.org/10.1145/3613904.3642186
Q&A platforms have been crucial for the online help-seeking behavior of programmers. However, the recent popularity of ChatGPT is altering this trend. Despite this popularity, no comprehensive study has been conducted to evaluate the characteristics of ChatGPT’s answers to programming questions. To bridge the gap, we conducted the first in-depth analysis of ChatGPT answers to 517 programming questions on Stack Overflow and examined the correctness, consistency, comprehensiveness, and conciseness of ChatGPT answers. Furthermore, we conducted a large-scale linguistic analysis, as well as a user study, to understand the characteristics of ChatGPT answers from linguistic and human aspects. Our analysis shows that 52% of ChatGPT answers contain incorrect information and 77% are verbose. Nonetheless, our user study participants still preferred ChatGPT answers 35% of the time due to their comprehensiveness and well-articulated language style. However, they also overlooked the misinformation in the ChatGPT answers 39% of the time. This implies the need to counter misinformation in ChatGPT answers to programming questions and raise awareness of the risks associated with seemingly correct answers.
https://doi.org/10.1145/3613904.3642596
Natural language (NL) programming has become more approachable due to the powerful code-generation capability of large language models (LLMs). This shift to using NL to program enhances collaborative programming by reducing communication barriers and context-switching among programmers from varying backgrounds. However, programmers may face challenges during prompt engineering in a collaborative setting as they need to actively keep aware of their collaborators' progress and intents. In this paper, we aim to investigate ways to assist programmers’ prompt engineering in a collaborative context. We first conducted a formative study to understand the workflows and challenges of programmers when using NL for collaborative programming. Based on our findings, we implemented a prototype, CoPrompt, to support collaborative prompt engineering by providing referring, requesting, sharing, and linking mechanisms. Our user study indicates that CoPrompt assists programmers in comprehending collaborators' prompts and building on their collaborators’ work, reducing repetitive updates and communication costs.
https://doi.org/10.1145/3613904.3642212
Almost no modern software system is written from scratch, and developers are required to effectively learn to use third-party libraries and software services. Thus, many practitioners and researchers have looked for ways to create effective documentation that supports developers' learning. However, few efforts have focused on how people actually use the documentation. In this paper, we report on an exploratory, multi-phase, mixed methods empirical study of documentation page-view logs from four cloud-based industrial services. By analyzing page-view logs for over 100,000 users, we find diverse patterns of documentation page visits. Moreover, we show statistically that which documentation pages people visit often correlates with user characteristics such as past experience with the specific product, on the one hand, and with future adoption of the API on the other hand. We discuss the implications of these results on documentation design and propose documentation page-view log analysis as a feasible technique for design audits of documentation, from ones written for software developers to ones designed to support end users (e.g., Adobe Photoshop).
https://doi.org/10.1145/3613904.3642721