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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. 

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. 

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.