On Developers’ Personality in Large-scale Distributed Projects: The Case of the Apache Ecosystem
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ICGSE 2018 presentation
![Personality
“A dynamic and organized set of traits
that create the unique patters of
behaviors, thoughts, and feelings of a
person"
[1] Ryckman, R. (2004). Theories of personality.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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On Developers’ Personality in Large-scale Distributed Projects: The Case of the Apache Ecosystem
- 1. On Developers’ Personality in Large-scale Distributed Projects: The Case of the Apache Ecosystem Fabio Calefato Giuseppe Iaffaldano Filippo Lanubile University of Bari, Italy Bogdan Vasilescu CMU, USA ICGSE 2018 29 May 2018 - Gothenburg, Sweden
- 2. Personality “A dynamic and organized set of traits that create the unique patters of behaviors, thoughts, and feelings of a person" [1] Ryckman, R. (2004). Theories of personality.
- 3. Why study developers’ personality © FinancesOnline.com EUREKA
- 5. Personality framework FacetsTraits Big 5 Agreeableness Trust, Cooperation, Altruism, Sympathy, Modesty, Moralism Extraversion Friendliness, Gregariousness, Assertiveness, Activity level, Excitement-seeking, Cheerfulness Conscientiousness Self-efficacy, Orderliness, Dutifulness, Achievement-striving, Self-discipline, Cautiousness Neuroticism (emotional stability) Anxiety, Anger, Depression, Self- consciousness, Immoderation, Vulnerability Openness to experiences Imagination, Artistic interests, Emotionality, Adventurousness, Intellect, Liberalism
- 6. Findings from previous research in psycholinguistic • Strong connections between language use and personality • Personality traits can be successfully derived from the analysis of written text (e.g., emails,…) [1, 2] • Every trait in the Big-Five model strongly and significantly associated with theoretically- appropriate patterns of word use [3] [1] Hirsh & Peterson, (2009) “Personality and language use in self-narratives” [2] Shen et al. (2013) “Understanding email writers: personality prediction from email messages” [3] Tausczik & Pennebaker (2010) “The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods”
- 7. Psychometric analysis tools • Linguistic Inquiry and Word Count (LIWC - "Luke") – Text classifier, counts %word falling into 80+ categories (linguistic, psychological, topical) – Used to prove strong association between linguistic patterns and personality or psychological state • IBM Personality Insights – Web service to perform linguistic analytics to infer individuals' personality from written communication – Builds on Big 5 and LIWC ICGSE 2018 29 May 2018 - Gothenburg, Sweden
- 8. Personality types of Apache developers github.com/collab-uniba/personality 176 projects mbox archives 106 mailing lists ~1.35M emails ~38K senders git clones 56 projects ~206K commits ~5K developers alias unmasking unique email addresses unique login / email addresses ~46K developer UIDs O C E A N
- 9. 5 Research questions Does developers’ personality… 1. change over time? 2. vary between core vs. peripheral developers? 3. vary with the degree of development activity? 4. change after becoming a core member? And finally... 5. What personality traits are associated with the likelihood of becoming a contributor? ICGSE 2018 29 May 2018 - Gothenburg, Sweden
- 10. RQ1: change over time Our study • Compared Big5 personality scores over multiple years • Apache developers evolve as – More open – More agreeable – More neurotic Related work ICGSE 2018 29 May 2018 - Gothenburg, Sweden • 400 active GitHub developers evolve as – More conscientious and extrovert – Less agreeable
- 11. RQ2: core vs. peripheral developers Our study • Paired comparisons between core and peripheral developers – Peripheral = devs w/o write access – Core = devs w/ write access to repo • No differences observed Related work ICGSE 2018 29 May 2018 - Gothenburg, Sweden • The 2 core developers responsible of 2007’s releases more extroverted and open than average
- 12. RQ3: change with degree of contribution Our study • Refinement of RQ2 • Paired comparisons between – High vs. low commit authors (peripheral) – High vs. Low commit integrators (core) • No differences observed Related work • High contributors are – more open, conscientious, extraverted, and neurotic – less agreeable ICGSE 2018 29 May 2018 - Gothenburg, Sweden
- 13. RQ4: change with project membership • Refinement of RQ2 • Devs’ personality scores grouped by before vs. after gaining write access to project repos (i.e., becoming a core project member) • No differences found Paired groups Trait V p-value Cliff’s Before vs After becoming core-team members Openness 70 0.5 0.07 Conscientiousness 90 0.2 0.15 Extroversion 50 0.5 -0.12 Agreeableness 50 0.5 -0.16 Neuroticism 70 0.5 0.18 Wilcoxon Signed-Rank test
- 14. RQ5: Likelihood of becoming contributor – Controls: word count + project size, age, and category not significant – Model performance: R2=.14, AUC=.82 Logistic regression model Openness Conscientiousness Agreeableness Extraversion Neuroticism + +++
- 15. Open questions • How does devs’ personality mesh with – Culture (geographical bias?) – Context (projects, ecosystems, different tasks?) • Findings different than prior work – Despite relying on the same personality theory (assess psychometric tools reliability) ICGSE 2018 29 May 2018 - Gothenburg, Sweden
- 16. One answer • Do we need psychologists to help us conduct GSE research? – I do believe so! ICGSE 2018 29 May 2018 - Gothenburg, Sweden
- 17. On Developers’ Personality in Large-scale Distributed Projects: The Case of the Apache Ecosystem THANKS! @fcalefatoFabio Calefato github.com/collab-uniba/personalityPackage: Pre-print: tiny.cc/icgse18 ICGSE 2018 29 May 2018 - Gothenburg, Sweden
Editor's Notes
- Psychology research has very many definitions of personality, here’s the one I like the most among the ones used in studies related to software engineering the one by Rickman and others which define personality as..
- Softw. projects the results of collective efforts performed by multiple developers, the larger a project the more the people involved, each having their own different personality We argue that studying and hence understanding their personality, have the potentially explain some of the stakeholders’ behaviors in various contexts (how they affect task execution, and they collaborate with each others ) in turn explain some intricacies of software development
- We’re not the first to argue this though, the subtle Effects of personality have been studied for more than 40ys now. And yet, there is no clear understanding of such effects on software dev, not only distributed even co-located, there does not seem to be some baseline or acknowledged ”truth” to build on Long and difficult road ahead on this research path is that there are Many personality frameworks that have been used in the studies so far
- Myers-Briggs Type Indicator (MBTI) Kersey Temperament Sorter (KTS) Minnesota Multiphasic Personality Inventory (MMPI) Inventories of hundreds and hundreds of items, unrealistic to imagine adminstering those to open source devs, or even paid devs from companies, profile takes hours on multiple days. … Big 5 Theory (Five-Factor Model) Simiplicity made “actionable” Describe the 5 traits O C E A N
- Extracting personality from written communication Previous research on psycholinguistic has confirmed strong connections between language use and personality and that the personality traits can be successfully derived from the analysis of written text [13] such as emails [14]. In fact, Tausczik & Pennebaker [15] found that every trait in the Big-Five model is strongly and significantly associated with theoretically-appropriate patterns of word use, indicating.
- Quantitative analysis of personalities of developers from communication traces Mined ecosystem-level data from the Apache Software Foundation (ASF) projects Apache SF project directory retrieved: Project name Status (active, incubating, retired) Development language Category Repository URI (git, svn) Mailing-list URIs (dev, user)
- RQs borrowed from prior work that used LIWC and big5 theory
- With project tenure, developers in the Apache ecosystem over time grow more open and agreeable (more cooperative) and more neurotic (more prone to worry and feeling stressed but also more self-conscious) They used LIWC and Big5
- Our other findings also show a sort of ‘stability’ in developers’ personality traits, which are not affected by project membership
- Agreeableness and openness => cooperative Agreeableness: 61% of deviance in data explained Deviance (measure of goodness of fits of the model, % of deviance explained is an effect size measure) Rsquared low, model does not explain all the variance in the data, but high AUC indicates that the model has a quite high predictive power and can accurately predict whether an apache developer will become a contributor or not (i.e., have 1+ contribution merged in a project repo).
- Culture -> increase relevance for global sw eng Context -> make research more actionable Paid vs volunteers? Do they have exhibit personality profiles?
- Culture -> increase relevance for global sw eng Context -> make research more actionable Paid vs volunteers? Do they have exhibit personality profiles?
- With project tenure, developers in the Apache ecosystem over time grow more open and agreeable (more cooperative) and more neurotic (more prone to worry and feeling stressed but also more self-conscious) we found evidence that developers evolve as more open, agreeable, and neurotic. In other words, they tend to become, respectively: (i) more capable of expressing their feelings; (ii) more cooperative, altruistic, and prone to trust the others; (iii) more prone to worry, self-conscious, and susceptible to stress. Rastogi & Nagappan [26] found that GitHub developers’ personality changed over time, too. However, in contrast with our results, they found developers to evolve as more conscientious and extrovert, and less agreeable over two consecutive years. While further investigations are needed to explain these differences, we note these claims are made despite some negligible to small effect sizes (Cohen’s ¶) observed.