  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
automated 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.
