Filipe’s paper “An empirical study of same-day releases of popular packages in the npm ecosystem” was accepted for publication in the Empirical Software Engineering journal! Super congrats Filipe! This was a collaboration with Gustavo Oliva and Ahmed Hassan.

Abstract:
Within a software ecosystem, client packages can reuse provider packages as third-party libraries. The reuse relation between client and provider packages is called a dependency. When a client package depends on the code of a provider package, every change that is introduced in a release of the provider has the potential to impact the client package. Since a large number of dependencies exist within a software ecosystem, releases of a popular provider package can impact a large number of clients. Occasionally, multiple releases of a popular package need to be published on the same day, leading to a scenario in which the time available to revise, test, build, and document the release is restricted compared to releases published within a regular schedule. In this paper, our objective is to study the same-day releases that are published by popular packages in the npm ecosystem. We design an exploratory study to characterize the type of changes that are introduced in same-day releases, the prevalence of same-day releases in the npm ecosystem, and the adoption of same-day releases by client packages. A preliminary manual analysis of the existing release notes suggests that same-day releases introduce non-trivial changes (e.g., bug fixes). We then focus on three RQs. First, we study how often same-day releases are published. We found that the median proportion of regularly scheduled releases that are interrupted by a same-day release (per popular package) is 22%, suggesting the importance of having timely and systematic procedures to cope with same-day releases. Second, we study the performed code changes in same-day releases. We observe that 32% of the same-day releases have larger changes compared with their prior release, thus showing that some same-day releases can undergo significant maintenance activity despite their time-constrained nature. In our third RQ, we study how client packages react to same-day releases of their providers. We observe the vast majority of client packages that adopt the release preceding the same-day release would also adopt the latter without having to change their versioning statement (implicit updates). We also note that explicit adoptions of same-day releases (i.e., adoptions that require a change to the versioning statement of the provider in question) is significantly faster than the explicit adoption of regular releases. Based on our findings, we argue that (i) third-party tools that support the automation of dependency management (e.g., Dependabot) should consider explicitly flagging same-day releases, (ii) popular packages should strive for optimized release pipelines that can properly handle same-day releases, and (iii) future research should design scalable, ecosystem-ready tools that support provider packages in assessing the impact of their code changes on client packages.

See our Publications for the full paper.

Quang’s paper “Improving the Discoverability of Indie Games by Leveraging their Similarity to Top-Selling Games Identifying Important Requirements of a Recommender System” was accepted for publication at the International Conference on the Foundations of Digital Games (FDG) 2021! Super congrats Quang!

* Update July 23: This paper won a best paper award at FDG 2021!

Abstract:
Indie games often lack visibility as compared to top-selling games due to their limited marketing budget and the fact that there are a large number of indie games. Players of top-selling games usually like certain types of games or certain game elements such as theme, gameplay, storyline. Therefore, indie games could leverage their shared game elements with top-selling games to get discovered. In this paper, we propose an approach to improve the discoverability of indie games by recommending similar indie games to gamers of top-selling games. We first matched 2,830 itch.io indie games to 326 top-selling Steam games. We then contacted the indie game developers for evaluation feedback and suggestions. We found that the majority of them (67.9%) who offered verbose responses show positive support for our approach. We also analyzed the reasons for bad recommendations and the suggestions by indie game developers to lay out the important requirements for such a recommendation system. The most important ones are: a standardized and extensive tag and genre ontology system is needed to bridge the two platforms, the expectations of players of top-selling games should be managed to avoid disappointment, a player’s preferences should be integrated when making recommendations, a standardized age restriction rule is needed, and finally, the recommendation tool should also show indie games that are the least similar or less popular.

The paper can be downloaded here.

Markos’ paper “What Causes Wrong Sentiment Classifications of Game Reviews?” was accepted for publication in the IEEE Transactions on Games journal! Super congrats Markos! This was a collaboration with Dayi Lin and Abram Hindle.

Abstract:
Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques is still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this paper, we study how sentiment analysis performs on game reviews. We first report the results of a large scale empirical study on the performance of widely-used sentiment classifiers on game reviews. Then, we investigate the root causes for the wrong classifications and quantify the impact of each cause on the overall performance. We study three existing classifiers: Stanford CoreNLP, NLTK, and SentiStrength. Our results show that most classifiers do not perform well on game reviews, with the best one being NLTK (with an AUC of 0.70). We also identified four main causes for wrong classifications, such as reviews that point out advantages and disadvantages of the game, which might confuse the classifier. The identified causes are not trivial to be resolved and we call upon sentiment analysis and game researchers and developers to prioritize a research agenda that investigates how the performance of sentiment analysis of game reviews can be improved, for instance by developing techniques that can automatically deal with specific game-related issues of reviews (e.g., reviews with advantages and disadvantages). Finally, we show that training sentiment classifiers on reviews that are stratified by the game genre is effective.

See our Publications for the full paper.