Abstract

Technical Q\&A sites have become essential for software engineers as they constantly seek help from other experts to solve their work problems. Despite their success, many questions remain unresolved, sometimes because the asker does not acknowledge any helpful answer. In these cases, an information seeker can only browse all the answers within a question thread to assess their quality as potential solutions. We approach this time-consuming problem as a binary-classification task where a best-answer prediction model is built to identify the accepted answer among those within a resolved question thread, and the candidate solutions to those questions that have received answers but are still unresolved. In this paper, we report on a study aimed at assessing 26 best-answer prediction models in two steps. First, we study how models perform when predicting best answers in Stack Overflow, the most popular Q\&A site for software engineers. Then, we assess performance in a cross-platform setting where the prediction models are trained on Stack Overflow and tested on other technical Q\&A sites. Our findings show that the choice of the classifier and automatied parameter tuning have a large impact on the prediction of the best answer. We also demonstrate that our approach to the best-answer prediction problem is generalizable across technical Q\&A sites. Finally, we provide practical recommendations to Q\&A platform designers to curate and preserve the crowdsourced knowledge shared through these sites.

Autore Pugliese

• Calefato Fabio , Lanubile Filippo , Novielli Nicole

Tutti gli autori

• CALEFATO F.;LANUBILE F.;NOVIELLI N.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2018

ISSN

1382-3256

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile