@conference {1925,
	title = {Investigating developers{\textquoteright} email discussions during decision-making in Python language evolution},
	booktitle = {Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering  - EASE{\textquoteright}17},
	year = {2017},
	pages = {286 - 291},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Karlskrona, SwedenNew York, New York, USA},
	abstract = {Context: Open Source Software (OSS) developers use mailing lists as their main forum for discussing the evolution of a project. However, the use of mailing lists by developers for decision-making has not received much research attention. Objective: We have explored this issue by studying developers{\textquoteright} email discussions around Python Enhancement Proposals (PEPs). Method: Our dataset comprised 42,672 emails from six different mailing lists pertaining to PEP development. We performed multiple forms of analysis on these emails, involving both quantitative measures (e.g., frequency) and deeper analysis of specific PEP discussions (i.e., outlier analysis). Results: Out of three PEP types (Informational, Process and Standard Track), Standard Track PEPs attract a large amount of discussion (both in volume and average number of messages per proposal). Our study also identified specific PEP states and topics that generated a disproportionate amount of discussion. Conclusion: Our outcomes point to several opportunities for improving the management of an OSS team based on the knowledge generated from discussions. We have also identified several interesting avenues for future work such as identifying individuals or groups that present persuasive arguments during decision-making.},
	isbn = {9781450348041},
	doi = {10.1145/3084226.3084271},
	url = {https://www.researchgate.net/publication/317486549_Investigating_developers\%27_email_discussions_during_decision-making_in_Python_language_evolution},
	author = {Stanger, Nigel and Rainer, Austen and Licorish, Sherlock A. and Sharma, Pankajeshwara and Savarimuthu, Bastin Tony Roy}
}
@proceedings {1927,
	title = {Managing Hidden Dependencies in OO Software: a Study Based on Open Source Projects},
	year = {2016},
	month = {11/2016},
	abstract = {Dependency-based software change impact analysis is the domain concerned with estimating sets of artifacts impacted by a change to a related artifact. Research has shown that analysing the various class dependency types independently will not reveal a complete estimate of impact sets. Therefore, dependency types are combined to improve the precision of estimated when compared to impact sets. Software classes can be linked in different ways; for instance semantically, if their meaning is somewhat related or, structurally, if one class depends on the services of other classes. {\textquoteright}Hidden{\textquoteright} dependencies arise when two classes, linked structurally, do not share the same semantic namespace or when semantically dependent classes do not share a structural link. With the goal of revealing hidden dependencies during change impact analysis, we empirically investigated the interplay between structural and semantic class dependencies in object-oriented software systems. Results show that (i) semantic and structural links are significantly associated, (ii) the strengths of those links does not play a significant role and, (iii) a significant number of dependencies are hidden. We propose refactoring techniques to deal with hidden dependencies, based on existing design patterns. Our approach has the potential for reducing refactoring and testing effort.},
	author = {Nemitari Ajienka and Capiluppi, Andrea and Counsell, Steve}
}
@conference {1823,
	title = {"May the fork be with you": novel metrics to analyze collaboration on GitHub},
	booktitle = {Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics - WETSoM 2014},
	year = {2014},
	note = {"According to FLOSSmole [8] (Free Libre OpenSource Software)
statistics, GitHub had 191765 repositories publicly
available at May 2012."},
	pages = {37 - 43},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {New York, New York, USA},
	abstract = {Multi{\textendash}repository software projects are becoming more and
more popular, thanks to web{\textendash}based facilities such as GitHub.
Code and process metrics generally assume a single repository
must be analyzed, in order to measure the characteristics
of a codebase. Thus they are not apt to measure how
much relevant information is hosted in multiple repositories
contributing to the same codebase. Nor can they feature
the characteristics of such a distributed development process.
We present a set of novel metrics, based on an original
classification of commits, conceived to capture some interesting
aspects of a multi{\textendash}repository development process. We
also describe an efficient way to build a data structure that
allows to compute these metrics on a set of Git repositories.
Interesting outcomes, obtained by applying our metrics
on a large sample of projects hosted on GitHub, show the
usefulness of our contribution.},
	keywords = {flossmole, github},
	isbn = {9781450328548},
	doi = {10.1145/2593868.2593875},
	url = {http://marbiaz.github.io/docs/Biazzini14b.pdf},
	author = {Marco Biazzini and Benoit Baudry}
}
