Extracting Development Tasks to Navigate Software Documentation

Abstract

Abstract-Knowledge management plays a central role in many software development organizations. While much of the important technical knowledge can be captured in documentation, there often exists a gap between the information needs of software developers and the documentation structure. To help developers navigate documentation, we developed a technique for automatically extracting tasks from software documentation by conceptualizing tasks as specific programming actions that have been described in the documentation. More than 70% of the tasks we extracted from the documentation of two projects were judged meaningful by at least one of two developers. We present TaskNavigator, a user interface for search queries that suggests tasks extracted with our technique in an auto-complete list along with concepts, code elements, and section headers. We conducted a field study in which six professional developers used TaskNavigator for two weeks as part of their ongoing work. We found search results identified through extracted tasks to be more helpful to developers than those found through concepts, code elements, and section headers. The results indicate that task descriptions can be effectively extracted from software documentation, and that they help bridge the gap between documentation structure and the information needs of software developers.

Augmenting API documentation with insights from stack overflow

“…In many of these cases, the background of the users seems to determine whether they understand a sentence or not. We found a similar situation in our previous work [36] when we asked developers to rate the meaningfulness of task descriptions that we had automatically extracted from their software documentation. In those cases, we argue that displaying such sentences does little harm if some users do not understand them while other users find them useful.…”

“…We had developed a set of techniques for preprocessing software documentation in previous work [36,37]. We summarize them here for completeness.…”

Choosing an NLP Library for Analyzing Software Documentation: A Systematic Literature Review and a Series of Experiments

“…One threat to the validity of our results and an opportunity for future work lies in the fact that we used all four NLP libraries with their default settings 9 and without any specialized models. Also, the results are only reflecting the performance and accuracy of the current library versions which might change as the libraries are evolving.…”

“…While it is common for researchers to rely on publicly available NLP libraries, some researchers develop their own tooling for specific tasks. For example, Allamanis et al [7] developed a customized system called Haggis for mining code idioms and in our own previous work, we added customizations to the Stanford NLP library to improve the accuracy of parsing natural language text authored by software developers [8], [9]. In this work, we aim to identify how the choice of using a particular publicly available NLP library could impact the results of any research that makes use of an NLP library.…”

Challenges in Analyzing Software Documentation in Portuguese

“…Several tools have been developed that automatically process natural language documents produced by software developers, for example by inferring specification from documentation [29], linking information from bug tracking systems and mailing lists to source code methods [14], summarizing bug reports [19], or extracting tasks from documentation [25]. Many of these tools rely on natural language processing tools such as the Stanford natural language processing toolkit [13] to split sentences, detect words in a sentence, assign parts of speech to words (such as adjective, verb, or noun), and to detect grammatical dependencies between different parts of a sentence (such as subject or direct object).…”