Insights for practitioners into empirical software architecture research

By: Matthias Galster, Danny Weyns

Associate Editor: Mehdi Mirakhorli (@MehdiMirakhorli)

Software architecture is an applied discipline. Therefore, research in that area should eventually offer practitioners useful and trustworthy results.

What we did: To find out more about the state of empirical software architecture research and its relevance for practitioners, we looked at research papers published between 1999 and 2015 at the most popular architecture conferences:

·       CBSE (International ACM SIGSOFT Symposium on Component-based Software Engineering)

·       ECSA (European Conference on Software Architecture)

·       QoSA (International ACM SIGSOFT Conference on the Quality of Software Architectures)

·       WICSA (Working IEEE/IFIP Conference on Software Architecture)

We went through a total of 667 “full” papers out of which 115 (i.e., around 17%) turned out to be “empirical”. In other words, 17% presented research based on systematically collected evidence from observation or experience [1], using methods such as case studies, experiments or surveys (the remaining 83% of papers present solutions or new methods without evaluation, experience reports, tools, etc.). The full study has been published as a research paper at WICSA 2016 [2] and a preprint is available [5]. In this post, we briefly highlight some of the insights.

Insight 1 – Amount of empirical work is increasing: Since around 2010, there is an increasing trend to publish empirical research in software architecture. The figure below shows the relative contribution of empirical papers compared to all papers published in a year.

Empirical methods are used equally to (a) to study phenomena (e.g., to understand how architecture decisions are made in practice) and (b) to evaluate approaches (e.g., to evaluate a new approach for capturing architectural knowledge). Therefore, current empirical research helps us understand software architecture practice (for example, to identify the real problems that research should tackle), but also contributes to increasing the confidence in the validity of newly proposed approaches and methods.

Insight 2 – Involvement of practitioners as researchers/co-authors is limited: On average, every two papers have one industry author but five authors from academia. Around 70% of papers have no industry author at all. Overall, empirical papers have roughly the same number of authors from industry than non-empirical papers. Little active involvement of practitioners (assuming that co-authors are actively involved in the research presented in a paper) might be due to challenges related successful industry-academia collaborations (e.g., champions in industry organizations, buy-in support commitment to contribute to industry needs) [3].

Insight 3 – Involvement of practitioners as study participants increases: Half of the empirical studies involve humans as subjects or study participants (e.g., in as participants in case studies or respondents of surveys). Most of these studies (68%) involve practitioners from industry (with the exception of experiments where students are the dominating participants). This involvement of practitioners is one way to increase the applicability and relevance of research findings for practice.

Insight 4 – Trustworthiness and rigor vary: Most studies (60%) discuss validity threats and acknowledge limitations. On the other hand, we should be careful when interpreting research findings: Many so-called “evaluations” presented in papers are often only illustrations using toy examples. Also, replications to confirm or refute previous findings and to build our confidence in research outcomes are almost non-existent in software architecture research. There might be a misconception in the research community that replications have little scientific value.

Insight 5 – There is a potential mismatch between research topics and trends in industry: Component selection and composition, architecture reasoning and decisions are dominating themes in empirical papers in the period 1999-2015. Michael Keeling in his reflection on emerging trends at SATURN [4] found architecting for DevOps, flexible design, lightweight architecture design methods and a renewed interest in architecture fundamentals as trends at SATURN. We couldn’t find these topics as trends in empirical papers.

Summary: Applying empirical methods puts an increasing demand on planning, conducting and evaluating research and its results, and presenting it in a way that is accessible and meaningful for practitioners. On the other hand, empirical research allows practitioners to get actively involved in collaborations with academics, both to explore emerging phenomena of interest (and to find out about the real problems in software architecture practice) and to evaluate new approaches that address practically relevant problems.

References:

1. D. Sjoberg, T. Dyba, and M. Jorgensen, "The Future of Empirical Methods in Software Engineering Research," in Future of Software Engineering (FOSE) Minneapolis, MN: IEEE Computer Society, 2007, pp. 358-378.
2. M. Galster and D. Weyns, "Empirical Research in Software Architecture - How far have we come?," in 13th Working IEEE/IFIP Conference on Software Architecture (WICSA) Venice, Italy: IEEE Computer Society, 2016.
3. C. Wohlin, A. Aurum, L. Angelis, L. Philips, Y. Dietrich, T. Gorschek, H. Grahn, K. Henningsson, S. Kagstrom, G. Low, P. Rovegard, P. Tomaszewski, C. Van Toorn, and J. Winter, "The Success Factors Powering Industry-Academia Collaboration," IEEE Software, vol. 29, pp. 67-73, 2012.
4. M. Keeling, "Lightweight and Flexible - Emerging Trends in Software Architecture from the SATURN Conferences," IEEE Software, vol. 32, pp. 7-11, 2015.
5. https://people.cs.kuleuven.be/danny.weyns/papers/2016WICSA.pdf

You may also like:

• F. Shull, "Research 2.0?," in IEEE Software, vol. 29, no. 6, pp. 4-8, Nov.-Dec. 2012.
  doi: 10.1109/MS.2012.164.
• Colin Potts. 1993. Software-Engineering Research Revisited. IEEE Software 10, 5 (September 1993), 19-28. DOI=http://dx.doi.org/10.1109/52.232392
• Tore Dyba, Barbara A. Kitchenham, and Magne Jorgensen. 2005. Evidence-Based Software Engineering for Practitioners. IEEE Software, 22, 1 (January 2005), 58-65. DOI=http://dx.doi.org/10.1109/MS.2005.6

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Has empiricism in Software Engineering amounted to anything?

Associate Editor: Bogdan Vasilescu, University of California, Davis. USA (@b_vasilescu) 

A 2014 talk by Jim Larus, currently (after a long tenure at Microsoft Research) Dean of the School of Computer and Communication Sciences at EPFL, was circulated recently over email among some colleagues. In the talk, Larus reflects on success and failure stories of software engineering (SE) tools used at Microsoft over the years: interestingly, he lists the empirical work on data mining and failure prediction, from Microsoft Research, as one of the biggest successes, together with Windows error reporting; in contrast, Larus mentions both PREfast (static analysis) and SDV (formal verification) as examples of tools that didn't matter as much.

Needless to say, this sparked quite the controversy among participants in my email thread, about the value (or lack thereof) of empiricism in SE. I found the discussion thoughtful and useful, and I’d like to share it. This blog post is an attempt to summarize two main arguments, drawing mostly verbatim from the email thread participants. Active participants included, at various times, Alex Wolf, David Rosenblum, Audris Mockus, Jim Herbsleb, Vladimir Filkov, and Prem Devanbu.

Is empiricism just blind correlation?

The main criticism is that empirical SE work does not move past mere correlations to find deeper understanding, yet it's understanding that leads to knowledge, not correlation. The analysis of data should enable understanding, explanation, and prediction, not be an end in and of itself. The accessibility of correlation among statistical methods, coupled with the flood of data from open-source development and social programming websites, have created a recipe for endless paper milling. As a result, many empirical SE papers usually don’t ask any critically important research questions, that matter both to researchers and practitioners; instead, they present obvious, expected, and unactionable insights, that neither researchers could exploit to develop new or improved approaches, nor practitioners to produce guaranteed improvements to their development efforts.

In response to this criticism, three counterexamples:

• Are automated debugging techniques actually helping programmers?

C Parnin, A Orso. ISSTA ’11 PDF

• On the naturalness of software

A Hindle, ET Barr, Z Su, M Gabel, P Devanbu. ICSE ’12, CACM Research Highlights ’16 PDF

• Are mutants a valid substitute for real faults in software testing?

R Just, D Jalali, L Inozemtseva, MD Ernst, R Holmes, G Fraser. FSE ’14 PDF

Arguably, the successes at Microsoft and other places, with saving money on quality control using process metrics, have led to a sort of Gold Rush and probably some excesses. However, it is clear that empirical SE has advanced leaps and bounds since the early correlation days. Admittedly, earlier work was trying to understand the usefulness of the data and stats, and resulted in some fluff; perhaps like with a new gadget, people were more interested in the technology than how useful it is in daily lives. However, thanks in part to all that playing around, we now have lots of solid work, and openness of the community to scientific methods.

Moreover, these days, to pass muster at top conferences, one needs to have a good theoretical grounding, not only on past work in SE & PL, but (often) also in sociology, psychology, management science, and behavioral science -- creating and maintaining software is inherently human-centric. Theory helps to predict and explain results; frame future research questions; provide continuity across studies and research groups; accumulate results into something resembling knowledge; and it points toward important open questions. For example, there is a lot of theory in psychology about multitasking and context switching, that really helps to explain the quantitative results relating productivity to patterns of work across projects. Such theory can both push further theory development as well as work toward deeper explanations of the phenomena we study. What exactly happens when humans context switch, why is context switching costly, and why do costs vary in different conditions?

This is also why correlations have largely fallen out of favour. Careful consideration of the relevant phenomena (in the context of existing work) will lead one to use, e.g., more sophisticated mixed-effects / multivariate models and bootstrapped distributions, and to carefully argue why their experimental methods are well-suited to validate the theory. The use of these models is driven precisely by more sophisticated theories (not statistical, but rather SE / behavioral science theories).

Can empiricism be valuable without fully understanding the “why”?

Although the demand for understanding why something works is prima facie not entirely unreasonable, if one insisted on that, then many engineering disciplines would come to a halt: e.g., airfoil design, medicine, natural language processing (no one really understands why n-gram models work better than just about anything else). Knowing that something works or doesn’t can be quite enough to help a great many people. We have many extremely useful things in the world that work by correlation and “hidden” models. For example, theoretically sound approaches such as topic models (e.g., LDA) don’t work well, but highly engineered algorithms (e.g., word2vec) work fantastically. By insisting on why, one would: stop using SVM, deep learning, many drugs; not fly on airplanes; and not use Siri/Echo, because in many cases the why of these things is not well understood. Even such a trivial thing as GPS has a lot of engineering adjustments that have not been derived from first principles but just work.

However, without explicit understanding of why things work, it’s not clear how far one can go with engineering. For example, many ancient civilizations were able to predict the phases of the moon and positions of the stars without understanding the why of gravity and orbital mechanics, simply based on repeated measurement. But would that have gotten us satellite communication, planetary exploration, and the Higgs boson? Similarly, would you feel comfortable in a plane that lands using a machine-learned autoland system?

Closing remarks

In summary, it seems there is a central place for empirical work in SE; not as an end in itself, but rather the beginning of an inquiry leading to understanding and useful insights that can we can use to truly advance our discipline. See also Peggy Storey's Lies, Damned Lies and Software Analytics talk along these lines.

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Using Biometrics to Assess a Developer's Cognitive and Emotional States

by Sebastian Müller (@smueller9, smueller@ifi.uzh.ch) and Thomas Fritz (fritz@ifi.uzh.ch)
Associate Editor: Christoph Treude (@ctreude)

What difficulties and emotions do software developers experience during their work? When is a developer "in the flow" and making a lot of progress? So far, little is known about these kinds of questions or the cognitive and emotional states of developers during work. Researchers have predominantly focused on the tangible output of developers' work, such as the source code, to identify bug-prone parts. Yet, a better understanding of what developers experience in their work would allow us to provide better and instantaneous support to developers, for instance by avoiding costly interruptions when a developer is in the flow or by intervening when a developer perceives particularly high difficulty levels and might be close to implementing a bug into the code.

Recent advances in biometric (aka psychophysiological) sensor technology in combination with research in psychology show promise that biometric data can fill this gap and provide answers to some of these kinds of questions. For instance, measures of a person's skin conductivity (aka EDA)—how much a person is sweating—can tell us something about the person's stress level and cognitive effort, but also perceived emotions.

In our research, we investigate the use of biometric measurements in the context of software engineering. In particular, we conduct studies ranging from short controlled lab studies to several week long field studies in which we examine the link between a developer's biometric measures and cognitive and emotional aspects of a developer while working on a change task, such as the perceived difficulty of source code elements or tasks, and the emotions and progress the developer experiences. Our vision is to use biometric measurements to gain a better understanding of what developers experience in their work and to use this improved understanding to better support developers.

To study the potential of biometrics to predict developers' perceived progress and emotions (valence), for example, we conducted a lab study [1] with 17 participants from two companies and one university. For this study, we collected biometric data, such as EDA, or the heart rate of participating developers while they were working on two different change tasks. At the same time, we asked developers in periodic intervals to self-report their perceived progress and emotions, specifically valence and arousal (on two scales from -200 to +200). The following figure highlights some of the collected data, with the biometric EDA and heart rate data on top and the self-reported valence and progress on bottom.

The figure also illustrates a visible difference in the biometric data, in particularly in the EDA signal, between the first episode that was rated as medium progress and higher valence compared to the last episode with the developer reporting being stuck and a lower valence. To analyze the data, we performed several steps of data cleaning, noise filtering and feature extraction, before we could feed these features into a machine learning classifier. The results of our analysis show that biometric data can be used well to distinguish between positive and negative emotions, as well as between phases of low and high progress, in most cases.

Overall, the results of our three studies ([1], [2], [3]) show that we are able to predict aspects of developers' cognitive and emotional states in real-time using biometrics. The results also show that we are able to make accurate predictions in the field in the common work environment of professional software developers and over multiple days. Not surprisingly, biometric data is very sensitive to the individual and certain biometric measures work better for some than for others. By using an approach that takes into account these individual differences more explicitly, we should be able to further improve our results in the future.

So what can we do with these insights? Similar to other metrics, such as code metrics or interaction metrics, biometrics can be leveraged to provide better support to developers, even in real-time. The results of our latest study [3] show that we can use biometrics to predict where quality concerns are or will occur while developers are still working on the change task, and that the biometrics even outperform more traditional metrics. These insights can now be used to enhance existing tool support, such as the automatic detection of quality concerns, to highlight and rank places in the code that should be reviewed and/or refactored, or even to indicate to the developer when he/she should reconsider a change to prevent him/her from ever submitting code with errors to the repository. Similarly, we can use biometrics to provide better interruption management support for developers or to provide recommendations when a developer perceives difficulty, ranging from talking to a coworker to taking a break.

In summary, the results of our studies are promising and provide initial evidence that biometrics can be used to assess cognitive and emotional states of a developer and thus help answer some of the open questions. As one example, our results show that we might be able to support developers when they are experiencing difficulties and help them to fix quality concerns early on. With the rapid and continuous advances in sensor technologies and the increasing pervasiveness of smart wearable devices that already collect biometric data, we might soon be able to leverage this kind of data for each professional software developer and fine-tune our classifiers to further increase their accuracy. At the same time, this opens up many ethical and privacy concerns that will have to be addressed and investigated in further studies.

References

1. Sebastian Müller and Thomas Fritz. Stuck and Frustrated or In Flow and Happy: Sensing Developers' Emotions and Progress. In Proceedings of the 37th International Conference on Software Engineering (ICSE 2015), Florence, Italy
2. Thomas Fritz, Andrew Begel, Sebastian Müller, Serap Yigitt-Elliott, and Manuela Züger. Using psycho-physiological measures to assess task difficulty in software development. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014), Hyderabad, India
3. Sebastian Müller and Thomas Fritz. Using Bio(Metrics) To Predict Code Quality Online. In Proceedings of the 38th International Conference of Software Engineering (ICSE 2016), Austin, USA

If you like this article, you might also enjoy reading:

• Graziotin, D., Wang, X., and Abrahamsson, P. Software Developers, Moods, Emotions, and Performance. In IEEE Software, vol.31, no.4. 2014
• Ikutani, Y. and Uwano, H. Brain activity measurement during program comprehension with NIRS. In Proceedings of the International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. 2014.
• Nakagawa, T., Kamei, Y., Uwano, H., Monden, A., Matsumoto, K., and German, D. M. Quantifying programmers' mental workload during program comprehension based on cerebral blood flow measurement: A controlled experiment. In Companion Proceedings of International Conference on Software Engineering. 2014.
• Parnin, C. Subvocalization - toward hearing the inner thoughts of developers. In Proceedings of the International Conference on Program Comprehension. 2011.
• Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., Saake, G., and Brechmann, A. Understanding understanding source code with functional magnetic resonance imaging. In Proceedings of the 36th International Conference on Software Engineering. 2014.

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The impact of FOSS on software engineering research

by Jesus M. Gonzalez-Barahona
Associate Editor: Stefano Zacchiroli

Back in year 2000, during Christmas, I had some time to play. Some days earlier, by chance, I had read "Estimating Linux's Size", a study on the number of lines of code of all the software in Red Hat 6.2, one of the most popular Linux-based distribution of the age. I had noticed that the tool used in the study, sloccount, was FOSS (free, open source software), so I could use it in my playground. For some days, my laptop was busy downloading all of Debian GNU/Linux 2.2 (Potato) source code, running sloccount on it, and producing all kinds of numbers, tables, and charts. At that time Debian was one of the largest, if not the largest, coordinated collections of software ever compiled. That fun lead to a series of paper, starting with "Counting potatoes: the size of Debian 2.2", and to a pivot in my research career that put me in the track of mining software repositories.
This personal story is an illustration of two emerging trends that proved to be game-changers: the public availability of information about software development, including the source code, and the availability of FOSS tools to retrieve and analyze that information. Both were enabled by FOSS, but they have worked in very different, yet complementary, ways.

FOSS as a matter of study

Everybody knows nowadays that FOSS projects release publicly the source code they produce. This has tremendously lowered the barriers to study software products, when that study requires access to their source code. No longer it is compulsory to have special agreements with software companies to research in this area. For the first time in the software history, it is possible and relatively easy to run comparative studies on large and very large samples of products.
Of course, the impact of these new opportunities has been gradual. As FOSS has become more and more relevant, more "interesting", industry-grade products have been available for studying. With time, researchers have developed new techniques and tools, and even a kind of a new mindset, for analyzing this new, yet very rich, corpus of cases.
Among FOSS projects, there is a large subset using an open development model. They keep public repositories for source code management, issue tracking, mailing lists and forums, etc. For them not only the source code, but also a lot of very detailed information about the development process is available. From the researcher point of view, this means a heaven of data to explore.
Already in the late 1990s some researchers started to take advantage of this new situation, but it was in the early 2000s when this new approach started to take shape, with works such as "Evolution in open source software: a case study", "Results From Software Engineering Research Into Open Source Development Projects Using Public Data","A Case Study of Open Source Software Development: The Apache Server", and "Open source software projects as virtual organizations".
Soon, specialized venues that dealt mostly with the study of data from software development repositories emerged. Among them, the International Working Conference on Mining Software Repositories, held annually since 2004, was one of the first and is today probably the most well known. During the last decade, this research approach has permeated all major conferences and journals in software engineering.

FOSS tools and public datasets as enablers of research

The availability of FOSS tools has been an enabler in many research fields, and software engineering has been no exception. The availability of efficient and mature FOSS databases, analysis tools, etc., allows any researcher, even with very modest budgets, to carry on ambitious studies with very large datasets. In addition, more and more tools specifically developed to analyze software or software repositories are available as FOSS. Many researchers are taking as customary to publish as FOSS the tools they use to produce the results shown in the data, letting others modify or adapt them to extend the research results. Some of them are useful for practitioners too.
For example, several FOSS tool sets exist to retrieve information from software repositories and organize it in databases, ready for analysis, such as MetricsGrimoire. They allow researchers to focus on the analysis of the data, and not on the retrieval, a task which may be very effort-consuming, specially when a large number of projects are involved. Practitioners are using them as well, to track KPIs (Key Performance Indicators) of their projects.
One step further, in some cases the datasets resulting from large data retrieval efforts are made public, letting researchers directly jump to analyze them. This is for example the case of the Boa Infrastructure, which allows to execute queries against hundreds of thousands of software projects very efficiently, or FLOSSMole, a repository of FOSS project data coming from several software development forges.

The impact on software engineering research

These two trends are changing large areas in the field of software engineering research, by allowing researchers to produce results in ways closer to the scientific method than before. With relatively little effort, they can start from solid data, which is becoming more and more accessible, and rely on mature, adaptable tools, to produce results that take into account a large diversity of cases. Some concrete impacts are:

• Availability of datasets. Researchers can work on the same dataset with different techniques, which eases the comparison of results, and allows to determine to which extent a study advances the state of the art. They also allow researchers to focus on analysis, and not on the assembly of the dataset itself, which is complex, error-prone, and effort-consuming.
• Reproducibility of the studies. When the tools used by researchers are FOSS, and the datasets are public, reproducing previous results becomes possible and even easy. This is already improving the chances of validating results, which is fundamental to advance on solid ground.
• Incremental research. Now it is much easier to build on the shoulders of giants, by reusing FOSS tools and datasets produced by other research teams. Researchers no longer have to start from the ground up, they can incrementally improve precious results.

In short, FOSS is making empirical software research much easier. Some clear examples are the advances in the areas of detection of code clones, the impact of different release models, or the limits to software evolution. The next years will let us know to which extent this is going to translate into results useful to improve our knowledge of how software is developed, and to improve software development itself.

References

"Estimating Linux's Size", David A. Wheeler, published online, November 6, 2000.
"Counting potatoes: the size of Debian 2.2", González-Barahona, Jesus M.; Pérez-Ortuño, Miguel; de las Heras Quirós, Pedro; Centeno-González, José; Matellán-Olivera, Vicent. Upgrade Magazine, 2(6), 60-66 (2001).
"Evolution in open source software: a case study", Godfrey, M.W.; Qiang Tu. Proceedings od the International Conference on Software Maintenance, 11-14 Oct. 2000, San Jose, CA, USA, 131-142.
"Results From Software Engineering Research Into Open Source Development Projects Using Public Data", Koch, Stefan; Georg Schneider, published online, 2000.
"A Case Study of Open Source Software Development: The Apache Server", Mockus, Audris; Fielding Roy; Herbsleb James.Proceedings of the International Conference on Software Engineering (ICSE 2000), 4-11 Jun. 2000, Limerick, Ireland.
"Open source software projects as virtual organizations: Competency rallying for software development", Crowston, K.; Scozzi, B. Proceedings of IEE Software, Feb. 2002, 149(1), 3-17.

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Supporting newcomers to Open Source Software

by Igor Steinmacher (@igorsteinmacher)
Associate Editor: Christoph Treude (@ctreude)

Community-based Open Source Software (OSS) projects leverage contributions from geographically distributed volunteers and require a continuous influx of newcomers for their survival, long-term success, and continuity. Therefore, it is essential to motivate, engage, and retain new developers in a project in order to promote a sustainable number of developers. Furthermore, recent studies report that newcomers are needed to replace older members leaving the project and are a potential source of new ideas and work procedures that the project needs [1].

However, new developers face various barriers when attempting to contribute [2]. In general, newcomers are expected to learn the social and technical aspects of the project on their own. Moreover, since delivering a task to an OSS project is usually a long, multi-step process, some newcomers may lose motivation and even give up contributing if there are too many barriers to overcome during this process. These barriers affect not only those interested in remaining project members, but also those who wish to submit a single contribution (e.g., a bug correction or a new feature). And, as Karl Fogel says in his book Producing Open Source Software: "If the project does not cause a good first impression, newcomers rarely give it a second chance."

To better support newcomers, it is necessary to identify and understand the barriers that prevent newcomers from contributing. With a better understanding of these barriers, it is possible to put effort towards building or improving tools and processes, ultimately leading to more contributions to the project. Therefore, we conducted a study to identify the barriers faced by newcomers [2][3]. We collected data from different sources: systematic literature review; open question responses gathered from OSS project contributors; students contributing to OSS projects; and interviews with experienced members and newcomers in OSS projects. Based on the analysis of this data, we organized 58 barriers into a model with 7 categories. Figure 1 depicts the categories and subcategories of this barriers model.


Figure 1. Barriers Model: Categories and Subcategories

Based on the barriers model, we built FLOSScoach, a portal to support the first steps of newcomers to OSS projects. The portal has been structured to reflect the categories identified in the barriers model. Each category was mapped onto a portal section which contains information and strategies aimed at supporting newcomers in overcoming the identified barriers. To populate the portal, we collected already-existing strategies and information from interviews with experienced members and from manual inspection of the project web pages.

In the portal, newcomers find information on the skills needed to contribute to a project, a step-by-step contribution flow, the location of features (such as source code repository, issue tracker and mailing list), a list of newcomer friendly tasks (if provided by the project) and tips on how to interact with the community. Preliminary studies have shown that FLOSScoach helps newcomers, guiding them in their first steps and making them more confident in their ability to contribute to a project [4]. When we compared students' performance with and without FLOSScoach, we found a significant drop in terms of self-efficacy among students in the control group (not using FLOSScoach) while the self-efficacy of students using the tool remained at a high level. In addition, by analyzing diaries written during the contribution process, we found evidence that FLOSScoach made newcomers feel oriented and more comfortable with the process, while those who did not have access to FLOSScoach repeatedly reported uncertainty and doubt on how to proceed.


Figure 2. FLOSScoach screen showing the contribution flow (How to Start section)

Identifying and organizing the barriers and the development of FLOSScoach are the first steps towards supporting newcomers to OSS projects. A smooth first contribution may increase the total number of successful contributions made by a single contributor and, hopefully, the number of long-term contributors. According to the results of our studies conducted so far, the points that deserve more attention are facilitating local workspace setup and providing ways to find the correct set of artifacts to work on once a task is selected.

You can access newcomer support for a number of OSS projects on the FLOSScoach site at http://www.flosscoach.com.

References

1. R. E. Kraut, M. Burke, J. Riedl, and P. Resnick, “The Challenges of Dealing with Newcomers”, in Building Successful Online Communities: Evidence-Based Social Design, R. E. Kraut and P. Resnick, Eds. MIT Press, 2012, pp. 179–230.
2. I. Steinmacher, A. P. Chaves, T. Conte, and M. A. Gerosa, “Preliminary empirical identification of barriers faced by newcomers to Open Source Software projects”, in Proceedings of the 28th Brazilian Symposium on Software Engineering, 2014, pp. 1–10.
3. I. Steinmacher, T. Conte, M. A. Gerosa, and D. Redmiles, “Social Barriers Faced by Newcomers Placing Their First Contribution in Open Source Software Projects”, in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 2015, pp. 1379–1392.
4. I. Steinmacher, I. S. Wiese, T. Conte, and M. A. Gerosa, “Increasing the self-efficacy of newcomers to Open Source Software projects”, in Proceedings of the 29th Brazilian Symposium on Software Engineering, 2015, pp. 160–169.

If you like this article, you might also enjoy reading:

• F. Fagerholm, A. Sanchez Guinea, J. Borenstein, J. Munch, “Onboarding in Open Source Projects”, in IEEE Software 31, 6, 2014, pp. 54-61.
• M. Aberdour, “Achieving Quality in Open-Source Software”, in IEEE Software 24, 1, 2007, pp. 58-64.

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