How the Evolution of Emerging Collaborations Relates to Code Changes: An Empirical Study - ICPC 2014

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Developers contributing to open source projects spontaneously group into "emerging" teams, re ected by messages ex-changed over mailing lists, issue trackers and other communication means. Previous studies suggested that such teams somewhat mirror the software modularity. This paper empirically investigates how, when a project evolves, emerging teams re-organize themselves|e.g., by splitting or merging. We relate the evolution of teams to the les they change, to investigate whether teams split to work on cohesive groups of files. Results of this study conducted on the evolution history of four open source projects, namely Apache HTTPD, Eclipse JDT, Netbeans, and Samba provide indications of what happens in the project when teams reorganize. Specifically, we found that emerging team splits imply working on more cohesive groups of les and emerging team merges imply working on groups of les that are cohesive from structural perspective. Such indications serve to better understand the evolution of software projects. More important, the observation of how emerging teams change can serve to suggest software remodularization actions.

How the Evolution of Emerging
Collaborations Relates to
Code Changes:
An Empirical Study
Sebastiano Gerardo Massimiliano Rocco
Panichella Canfora Di Penta Oliveto

Outline
Analysis of Teams Evolution:
Teams identification from
Emergent Collaborations
Conclusion and Future Work
Context and Motivation:
Open Source Projects

Emergenging Teams in Open
Source Project

Emergenging Teams in Open
Source Project
Team
1
Team
2
Team
n
...

Emergenging Teams in Open
Source Project
Team
1
Team
2
Team
n
...
New Features
Bugs fixing
...................
...................
...................

Previous Work...
Hong Et al. - ICSM 2011

Analysis of the evolution
of Teams: why?

Analysis of the evolution
of Teams: why?
1) To better understand the Reasons
Behind the teams reorganization
(split/merge of developers teams)

Analysis of the evolution
of Teams: why?
1) To better understand the Reasons
Behind the teams reorganization
(split/merge of developers teams)
2) Investigate whether emerging teams evolve
with the aim of working on more cohesive
groups of files

Analysis of the evolution
of Teams: how?

Teams Identification from Emergent Collaborations
Analysis of the evolution
of Teams: how?

By use FUZZY
CLUSTER ALGORITHMS
Teams Identification from Emergent Collaborations
Analysis of the evolution
of Teams: how?

R1
By use FUZZY
CLUSTER ALGORITHMS
R2
Analysis of the evolution
of Teams: how?

TEAMS SPLIT
TEAMS MERGE
R1
By use FUZZY
CLUSTER ALGORITHMS
R2
Analysis of the evolution
of Teams: how?

R1
R2
By use FUZZY
CLUSTER ALGORITHMS
Analysis of the evolution
of Teams: how?

R1
R2
By use FUZZY
CLUSTER ALGORITHMS
Sub-system one Sub-system twoSub-systems two
Sub-System where developers working on....
Analysis of the evolution
of Teams: how?

R1
R2
By use FUZZY
CLUSTER ALGORITHMS
Sub-system one Sub-system twoSub-systems two
Sub-System where developers working on....
Mancoridis et al.
Modul. Quality
Poshyvanyk et al.
CCBC
Structure
Persprective
Conceptual
Persprective
Analysis of the evolution
of Teams: how?

Apache HTTP Eclipse JDT Netbeans Samba
Period
considered
09/1998-03/2012
01/2002-
12/2011
01/2001-
08/2012
01/2000-09/2011
Releases
Considered
2.0
2.2.0
2.2.4
2.2.12
2.4.1
3.0
3.2
3.4
3.6
4.2
3.4
3.6
5.5
6.9
7.2
2.3
3.0.20
3.0.25
3.5.0
4.0
Systems characteristics: Period of time and Releases Considered
Case study
• Goal: analyze data from mailing lists/issue trackers and
versioning systems
• Purpose: observe the reorganization of the teams
between releases
• MQ/CCBC: better understand (i) the reason behind the
reorganization of teams

Research Questions
RQ1: How do emerging collaborations change across
software releases?
RQ2: How does the evolution of emerging collaboration
relate to the cohesiveness of files changed by
emerging teams?

RQ1: How do emerging collaborations change across
software releases?

Teams Split in a new release :
- In 15%-35% of the
cases
RQ1: How do emerging collaborations change across
software releases?

Teams Merge in a new release :
- in 20%-35% of the
cases
Teams Split in a new release :
- In 15%-35% of the
cases
RQ1: How do emerging collaborations change across
software releases?

TEAMS
SPLIT
TEAMS
MERGED
RQ2 : How does the evolution of emerging collaboration relate to the cohesiveness
of files changed by emerging teams?

TEAMS
SPLIT
TEAMS
MERGED
RQ2 : How does the evolution of emerging collaboration relate to the cohesiveness
of files changed by emerging teams?
MQCCBC

How the Evolution of Emerging Collaborations Relates to Code Changes: An Empirical Study - ICPC 2014

Written communications recorded through channels such as mailing lists or issue trackers, but also code cochanges, have been used to identify emerging collaborations in software projects. Also, such data has been used to identify the relation between developers’ roles in communication networks and source code changes, or to identify mentors aiding newcomers to evolve the software project. However, results of such analyses may be different depending on the communication channel being mined. This paper investigates how collaboration links vary and complement each other when they are identified through data from three different kinds of communication channels, i.e., mailing lists, issue trackers, and IRC chat logs. Also, the study investigates how such links overlap with links mined from code changes, and how the use of different sources would influence (i) the identification of project mentors, and (ii) the presence of a correlation between the social role of a developer and her changes. Results of a study conducted on seven open source projects indicate that the overlap of communication links between the various sources is relatively low, and that the application of networks obtained from different sources may lead to different results.

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Information Retrieval methods have been largely adopted to identify traceability links based on the textual similarity of software artifacts. However, noise due to word usage in software artifacts might negatively affect the recovery accuracy. We propose the use of smoothing filters to reduce the effect of noise in software artifacts and improve the performances of traceability recovery methods. An empirical evaluation performed on two repositories indicates that the usage of a smoothing filter is able to significantly improve the performances of Vector Space Model and Latent Semantic Indexing. Such a result suggests that other than being used for traceability recovery the proposed filter can be used to improve performances of various other software engineering approaches based on textual analysis.

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The recent and fast expansion of OSS (Open-source software) communities has fostered research on how open source projects evolve and how their communities interact. Several research studies show that the inflow of new developers plays an important role in the longevity and the success of OSS projects. Beside that they also discovered that an high percentage of newcomers tend to leave the project because of the socio-technical barriers they meet when they join the project. However, such research effort did not generate yet concrete results in support retention and training of project newcomers. In this thesis dissertation we investigated problems arising when newcomers join software projects, and possible solutions to support them. Specifically, we studied (i) how newcomers behave during development activities and how they interact with others developers with the aim at (ii) developing tools and/or techniques for supporting them during the integration in the development team. Thus, among the various recommenders, we defined (i) a tool able to suggest appropriate mentors to newcomers during the training stage; then, with the aim at supporting newcomers during program comprehension we defined other two recommenders: a tool that (ii) generates high quality source code summaries and another tool able to (iii) provide descriptions of specific source code elements. For future work, we plan to improve the proposed recommenders and to integrate other kind of recommenders to better support newcomers in OSS projects.

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Sebastiano Panichella

NLBSE’22: Tool Competition

Sebastiano Panichella

Tool Competition Introduction NLP-based approaches and tools have been proposed to improve the efficiency of software engineers, processes, and products, by automatically processing natural language artifacts (issues, emails, commits, etc.). We believe that the availability of accurate tools is becoming increasingly necessary to improve Software Engineering (SE) processes. One important process is issue management and prioritization where developers have to understand, classify, prioritize, assign, etc. incoming issues reported by end-users and developers. This year, we are pleased to announce the first edition of the NLBSE’22 tool competition on issue report classification, an important task in issue management and prioritization. For the competition, we provide a dataset encompassing more than 800k labeled issue reports (as bugs, enhancements, and questions) extracted from real open-source projects. You are invited to leverage this dataset for evaluating your classification approaches and compare the achieved results against a proposed baseline approach (based on FastText). Competition overview We created a Colab notebook with detailed information about the competition (provided data, baseline approach, paper submission, paper format, etc.). If you want to participate, you must: Train and tune a multi-label multi-class classifier using the provided training set. The classifier should assign one label to an issue. Evaluate your classifier on the provided test set Write a paper (4 pages max.) describing: The architecture and details of the classifier The procedure used to pre-process the data The procedure used to tune the classifier on the training set The results of your classifier on the test set Additional info.: provide a link to your code/tool with proper documentation on how to run it Submit the paper by emailing the tool competition organizers (see below) Submissions will be evaluated and accepted based on correctness and reproducibility, defined by the following criteria: Clarity and detail of the paper content Availability of the code/tool, released as open-source Correct training/tuning/evaluation of your code/tool on the provided data Clarity of the code documentation The accepted submissions will be published at the workshop proceedings. The submissions will be ranked based on the F1 score achieved by the proposed classifiers on the test set, as indicated in the papers. The submission with the highest F1 score will be the winner of the competition. How to participate? Email your paper to Oscar Chaparro (oscarch@wm.edu) and Rafael Kallis (rk@rafaelkallis.com) by the submission deadline.

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How the Evolution of Emerging Collaborations Relates to Code Changes: An Empirical Study - ICPC 2014

1. How the Evolution of Emerging Collaborations Relates to Code Changes: An Empirical Study Sebastiano Gerardo Massimiliano Rocco Panichella Canfora Di Penta Oliveto

2. Outline Analysis of Teams Evolution: Teams identification from Emergent Collaborations Conclusion and Future Work Context and Motivation: Open Source Projects

3. Emergenging Teams in Open Source Project

4. Emergenging Teams in Open Source Project Team 1 Team 2 Team n ...

5. Emergenging Teams in Open Source Project Team 1 Team 2 Team n ... New Features Bugs fixing ................... ................... ...................

6. Previous Work... Hong Et al. - ICSM 2011

7. Previous Work... Bird Et al. - FSE 2008

8. Analysis of the evolution of Teams: why?

9. Analysis of the evolution of Teams: why? 1) To better understand the Reasons Behind the teams reorganization (split/merge of developers teams)

10. Analysis of the evolution of Teams: why? 1) To better understand the Reasons Behind the teams reorganization (split/merge of developers teams) 2) Investigate whether emerging teams evolve with the aim of working on more cohesive groups of files

11. Analysis of the evolution of Teams: how?

12. Teams Identification from Emergent Collaborations Analysis of the evolution of Teams: how?

13. By use FUZZY CLUSTER ALGORITHMS Teams Identification from Emergent Collaborations Analysis of the evolution of Teams: how?

14. R1 By use FUZZY CLUSTER ALGORITHMS R2 Analysis of the evolution of Teams: how?

15. TEAMS SPLIT TEAMS MERGE R1 By use FUZZY CLUSTER ALGORITHMS R2 Analysis of the evolution of Teams: how?

16. R1 R2 By use FUZZY CLUSTER ALGORITHMS Analysis of the evolution of Teams: how?

17. R1 R2 By use FUZZY CLUSTER ALGORITHMS Sub-system one Sub-system twoSub-systems two Sub-System where developers working on.... Analysis of the evolution of Teams: how?

18. R1 R2 By use FUZZY CLUSTER ALGORITHMS Sub-system one Sub-system twoSub-systems two Sub-System where developers working on.... Mancoridis et al. Modul. Quality Poshyvanyk et al. CCBC Structure Persprective Conceptual Persprective Analysis of the evolution of Teams: how?

19. Apache HTTP Eclipse JDT Netbeans Samba Period considered 09/1998-03/2012 01/2002- 12/2011 01/2001- 08/2012 01/2000-09/2011 Releases Considered 2.0 2.2.0 2.2.4 2.2.12 2.4.1 3.0 3.2 3.4 3.6 4.2 3.4 3.6 5.5 6.9 7.2 2.3 3.0.20 3.0.25 3.5.0 4.0 Systems characteristics: Period of time and Releases Considered Case study • Goal: analyze data from mailing lists/issue trackers and versioning systems • Purpose: observe the reorganization of the teams between releases • MQ/CCBC: better understand (i) the reason behind the reorganization of teams

20. Research Questions RQ1: How do emerging collaborations change across software releases? RQ2: How does the evolution of emerging collaboration relate to the cohesiveness of files changed by emerging teams?

21. RQ1: How do emerging collaborations change across software releases?

22. Teams Split in a new release : - In 15%-35% of the cases RQ1: How do emerging collaborations change across software releases?

23. Teams Merge in a new release : - in 20%-35% of the cases Teams Split in a new release : - In 15%-35% of the cases RQ1: How do emerging collaborations change across software releases?

24. Teams Merge in a new release : - in 20%-35% of the cases Teams Split in a new release : - In 15%-35% of the cases RQ1: How do emerging collaborations change across software releases? Teams desappeared: 22%-45% Teams Survived: 50%-70%

25. TEAMS SPLIT TEAMS MERGED RQ2 : How does the evolution of emerging collaboration relate to the cohesiveness of files changed by emerging teams?

26. TEAMS SPLIT TEAMS MERGED RQ2 : How does the evolution of emerging collaboration relate to the cohesiveness of files changed by emerging teams? MQCCBC

27. TEAMS SPLIT TEAMS MERGED RQ2 : How does the evolution of emerging collaboration relate to the cohesiveness of files changed by emerging teams? MQCCBC MQCCBC

28. TEAMS SPLIT TEAMS MERGED RQ2 : How does the evolution of emerging collaboration relate to the cohesiveness of files changed by emerging teams? MQCCBC MQCCBC

29. Future Work

Editor's Notes

Thus, this presentation is composed by three parts, a first part where, I will discuss about the context and motivation of our work and why it is important study teams evolution After that I ‘m going to present how we identify and analyze the evolution of emerging teams for several software projects… And finally I will discuss conclusions and future directions…. Fine… So the context of our preliminar study is the software development in open source project Where...
As we know an open source community can be seen as a complex self-organizing system in which developers contributing to open source projects spontaneously group into emerging" teams… …..emerging" teams that work together sharing knowledge and technical skills In order to Apply maintenance activity, like for example, add new features in the project, fix bugs and so on… With the main purpose to grow the project and ensure its success.. This has motivated a lot of research effort in mining the social networking among developers…
As we know an open source community can be seen as a complex self-organizing system in which developers contributing to open source projects spontaneously group into emerging" teams… …..emerging" teams that work together sharing knowledge and technical skills In order to Apply maintenance activity, like for example, add new features in the project, fix bugs and so on… With the main purpose to grow the project and ensure its success.. This has motivated a lot of research effort in mining the social networking among developers…
As we know an open source community can be seen as a complex self-organizing system in which developers contributing to open source projects spontaneously group into emerging" teams… …..emerging" teams that work together sharing knowledge and technical skills In order to Apply maintenance activity, like for example, add new features in the project, fix bugs and so on… With the main purpose to grow the project and ensure its success.. This has motivated a lot of research effort in mining the social networking among developers…
FOR EXAMPLE: The work by Hong at al. compare DSNs built considering as source issue trackers discussions with popular GSNs such as Facebook, Twitter, Cyworld. and found that while most social networks exhibit power law degree distributions, DSNs do not. where "Power-law node degree distribution" means to have only a few nodes with very high degree and a large number of nodes with low degree. We share with this work, the analysis of the DSNs evolution over time, and in particular, the analysis of the events within a project, such as a release of new software, tha impact the DSNs structure, for exampe if split/merge of developers sub-communities in the project are reflected in release of new software.
In the work of Bird et al. The authors analyze the developers community from mailinglists and they found how the sub communities identified using communication information are significantly connected with collaboration behavior. However, in summary, all these past works did not analyzed in details the evolution of the teams during the time, and for example “the reasons behind the reorganization of teams….”
So, we study and analyzed the evolution of teams with the porpuse To better understand the Reasons Behind the teams reorganization, Focusing on, split and merge of developers teams And Investigate whether emerging teams evolve with the aim of working on more cohesive groups of files
So, we study and analyzed the evolution of teams with the porpuse To better understand the Reasons Behind the teams reorganization, Focusing on, split and merge of developers teams And Investigate whether emerging teams evolve with the aim of working on more cohesive groups of files
In conclusion we analysed the evolution of emerging collaborations between developers with the aim at investigate how collaborations changes over the time, verifying the coesiveness of the changes performed by such teams. What we found is that…between releases there is a continuous Recombination of the teams, where however a conspicuous number of teams survive between subsequent release…” And The re-organization of developers into teams is reflected in cohesive changes occurring in the system structure…

How the Evolution of Emerging Collaborations Relates to Code Changes: An Empirical Study - ICPC 2014

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How the Evolution of Emerging Collaborations Relates to Code Changes: An Empirical Study - ICPC 2014

Editor's Notes