Identifying features in forks

Abstract

Fork-based development has been widely used both in open source communities and in industry, because it gives developers flexibility to modify their own fork without affecting others. Unfortunately, this mechanism has downsides: When the number of forks becomes large, it is difficult for developers to get or maintain an overview of activities in the forks. Current tools provide little help. We introduce Infox, an approach to automatically identify non-merged features in forks and to generate an overview of active forks in a project. The approach clusters cohesive code fragments using code and networkanalysis techniques and uses information-retrieval techniques to label clusters with keywords. The clustering is effective, with 90 % accuracy on a set of known features. In addition, a human-subject evaluation shows that Infox can provide actionable insight for developers of forks.

The state of adoption and the challenges of systematic variability management in industry

“…As such, a feature modeling process is needed, that supports obtaining a feature model from a diverse set of variability information sources (Bécan et al 2016;Davril et al 2013;Krueger et al 2019a), integrated with feature-model management, merging, and synthesis techniques (Acher et al 2010, b, She et al 2011. Our cases express that functional evolution is well reflected in commit messages (e.g., bug fix information, new feature implementations, new branches), which confirms some insights that commit messages are a good source for feature identification (Krueger et al 2018a(Krueger et al , 2019aZhou et al 2018).…”

How has forking changed in the last 20 years?

“…For example, P2 reports to only occasionally synchronize minor improvements, because the fork has diverged to much to synchronize larger changes; P10 has experienced problems of synchronizing too frequently and thus being faced with incomplete implementations and now only selectively synchronizes features of interest. In line with prior observations on monitoring change feeds [5,10,33,52], interviewees report that systematically monitoring changes from other repositories is onerous and that current tools like GitHub's network graph are difficult to use and does not scale (P11, P16).…”

“…They are a mechanism to contribute to a project, and most open-source projects actively embrace external contributors [19,46]. Although some maintainers complain about the burden of dealing with so many third-party contributions [21,46] and some researchers warn about inefficiencies regarding lost contributions or duplicate work [38,52,53], we are not aware of any calls to constrain social forking. Importantly though, as our study will show, the distinction between social and hard forks is fluent.…”

“…Tooling has changed significantly since the pre-GitHub days of prior studies on hard forks which may allow new forms of collaboration across forks: Git specifically supports merges across distributed version histories, as well as selectively integrating changes through a 'cherry picking' feature. GitHub and similar social coding pages track forks, allowing developers to subscribe to changes in select repositories, and generally make changes in forks transparent [10,11,52]. Essentially all interviewees were familiar with GitHub's network view [1] that visually shows contributions over time across forks and branches.…”

Normalization of Java Source Codes

“…There are different techniques or tools that aims at normalizing methods names in source codes. INFOX [9] is a tool that aims at tracking forks for a project on github. It then extracts features and clusters code snippets in order to highlight similar code.…”