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Conversational agents that respond to user information requests through a natural conversation have the potential to revolutionize how we acquire new information on the Web (i.e., perform exploratory Web searches). Recent advances to conversational search agents use popular Web search engines as a back-end and sophisticated AI algorithms to maintain context, automatically generate search queries, and summarize results into utterances. While showing impressive results on general topics, the potential of this technology for software engineering is unclear. In this paper, we study the potential of conversational search agents to aid software developers as they acquire new knowledge. We also obtain user perceptions of how far the most recent generation of such systems (e.g., Facebook's BlenderBot2) has come in its ability to serve software developers. Our study indicates that users find conversational agents helpful in gaining useful information for software-related exploratory search; however, their perceptions also indicate a large gap between expectations and current state of the art tools, especially in providing high-quality information. Participant responses provide directions for future work. 

The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted follow-up questions on GitHub. To estimate the best follow-up question for a specific deficient bug report we combine two metrics based on: 1) the compatibility of a follow-up question to a specific bug report; and 2) the utility the expected answer to the follow-up question would provide to the deficient bug report. Evaluation of our system, based on a manually annotated held-out data set, indicates improved performance over a set of simple and ablation baselines. A survey of software developers confirms the held-out set evaluation result that about half of the selected follow-up questions are considered valid. The survey also indicates that the valid follow-up questions are useful and can provide new information to a bug report most of the time, and are specific to a bug report some of the time. 

Virtual conversational assistants designed specifically for software engineers could have a huge impact on the time it takes for software engineers to get help. Research efforts are focusing on virtual assistants that support specific software development tasks such as bug repair and pair programming. In this paper, we study the use of online chat platforms as a resource towards collecting developer opinions that could potentially help in building opinion Q&A systems, as a specialized instance of virtual assistants and chatbots for software engineers. Opinion Q&A has a stronger presence in chats than in other developer communications, thus mining them can provide a valuable resource for developers in quickly getting insight about a specific development topic (e.g., What is the best Java library for parsing JSON?). We address the problem of opinion Q&A extraction by developing automatic identification of opinion-asking questions and extraction of participants’ answers from public online developer chats. We evaluate our automatic approaches on chats spanning six programming communities and two platforms. Our results show that a heuristic approach to opinion-asking questions works well (.87 precision), and a deep learning approach customized to the software domain outperforms heuristics-based, machine-learning-based and deep learning for answer extraction in community question answering. 

More than ever, developers are participating in public chat communities to ask and answer software development questions. With over ten million daily active users, Slack is one of the most popular chat platforms, hosting many active channels focused on software development technologies, e.g., python, react. Prior studies have shown that public Slack chat transcripts contain valuable information, which could provide support for improving automatic software maintenance tools or help researchers understand developer struggles or concerns. In this paper, we present a dataset of software-related Q&A chat conversations, curated for two years from three open Slack communities (python, clojure, elm). Our dataset consists of 38,955 conversations, 437,893 utterances, contributed by 12,171 users. We also share the code for a customized machine-learning based algorithm that automatically extracts (or disentangles) conversations from the downloaded chat transcripts. 

Software developers are increasingly having conversations about software development via online chat services. Many of those chat communications contain valuable information, such as code descriptions, good programming practices, and causes of common errors/exceptions. However, the nature of chat community content is transient, as opposed to the archival nature of other developer communications such as email, bug reports and Q&A forums. As a result, important information and advice are lost over time. The focus of this dissertation is Extracting Archival Information from Software-Related Chats, specifically to (1) automatically identify conversations which contain archival-quality information, (2) accurately reduce the granularity of the information reported as archival information, and (3) conduct a case study to investigate how archival quality information extracted from chats compare to related posts in Q&A forums. Archiving knowledge from developer chats that could be used potentially in several applications such as: creating a new archival mechanism available to a given chat community, augmenting Q&A forums, or facilitating the mining of specific information and improving software maintenance tools. 

Online tutorials are a valuable source of community created information used by numerous developers to learn new APIs and techniques. Once written, tutorials are rarely actively curated and can become dated over time. Tutorials often reference APIs that change rapidly, and deprecated classes, methods and fields can render tutorials inapplicable to newer releases of the API.Newer tutorials may not be compatible with older APIs that are still in use. In this paper, we first empirically study the tutorial versioning problem, confirming its presence in popular tutorials on the Web. We subsequently propose a technique, based on similar techniques in the literature, for automatically detecting the applicable API version ranges of tutorials, given access to the official API documentation they reference. The proposed technique identifies each API mention in a tutorial and maps the mention to the corresponding API element in the official documentation. The version of the tutorial is determined by combining the version ranges of all of the constituent API mentions. Our technique’s precision varies from 61% to 89% and recall varies from 42% to 84% based on different levels of granularity of API mentions and different problem constraints. We observe API methods are the most challenging to accurately disambiguate due to method overloading. As the API mentions in tutorials are often redundant, and each mention of a specific API element commonly occurs several times in a tutorial, the distance of the predicted version range from the true version range is low; 3.61 on average for the tutorials in our sample. 

Modern software development communities are increasingly social. Popular chat platforms such as Slack host public chat communities that focus on specific development topics such as Python or Ruby-on-Rails. Conversations in these public chats often follow a Q&A format, with someone seeking information and others providing answers in chat form. In this paper, we describe an exploratory study into the potential usefulness and challenges of mining developer Q&A conversations for supporting software maintenance and evolution tools. We designed the study to investigate the availability of information that has been successfully mined from other developer communications, particularly Stack Overflow. We also analyze characteristics of chat conversations that might inhibit accurate automated analysis. Our results indicate the prevalence of useful information, including API mentions and code snippets with descriptions, and several hurdles that need to be overcome to automate mining that information.