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What should I document? A preliminary systematic mapping study into API documentation knowledge

conference contribution
posted on 01.01.2019, 00:00 authored by Alex CummaudoAlex Cummaudo, Rajesh VasaRajesh Vasa, John Grundy
© 2019 IEEE. Background: Good API documentation facilitates the development process, improving productivity and quality. While the topic of API documentation quality has been of interest for the last two decades, there have been few studies to map the specific constructs needed to create a good document. In effect, we still need a structured taxonomy that captures such knowledge systematically.Aims: This study reports emerging results of a systematic mapping study. We capture key conclusions from previous studies that assess API documentation quality, and synthesise the results into a single framework.Method: By conducting a systematic review of 21 key works, we have developed a five dimensional taxonomy based on 34 categorised weighted recommendations.Results: All studies utilise field study techniques to arrive at their recommendations, with seven studies employing some form of interview and questionnaire, and four conducting documentation analysis. The taxonomy we synthesise reinforces that usage description details (code snippets, tutorials, and reference documents) are generally highly weighted as helpful in API documentation, in addition to design rationale and presentation.Conclusions: We propose extensions to this study aligned to developer utility for each of the taxonomy's categories.

History

Event

ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (2019 : Porto de Galinhas, Recife, Brazil)

Volume

2019-Septemer

Pagination

1 - 6

Publisher

IEEE

Location

Porto de Galinhas, Recife, Brazil

Place of publication

Piscataway, N.J.

Start date

19/09/2019

End date

20/09/2019

ISSN

1949-3770

eISSN

1949-3789

ISBN-13

9781728129686

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

ESEM 2019 : Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)