A Preliminary Analysis on the Effects of Propensity to Trust in Distributed Software Development
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This study analyzed the effects of an individual's propensity to trust on the successful collaboration in distributed software projects. The researchers collected data on 218 pull requests from one open source project involving 6 integrators. They used integrators' email language and the Big 5 personality model's agreeableness trait as a proxy for propensity to trust. Logistic regression found integrators with high propensity to trust were 34% more likely to accept pull requests, even after controlling for other factors. However, the study had limitations including focusing on one project and using agreeableness as the sole measure of propensity to trust. Further research on more projects is needed.
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A Preliminary Analysis on the Effects of Propensity to Trust in Distributed Software Development
- 1. A Preliminary Analysis on the Effects of Propensity to Trust in Distributed Software Development Fabio Calefato, Filippo Lanubile, Nicole Novielli University of Bari Italy ICGSE’17 May 22-23, 2017 – Buenos Aires, Argentina
- 2. A Bit of Theory on Trust INTEGRITY The adherence to intrinsic moral norms which makes a trustee reliable BENEVOLENCE The perceived level of courtesy and positive attitude ABILITY Capability of a trustee (based on knowledge, competence, skills) to perform tasks within a specific domain PREDICTABILITY The degree to which a person is liable and accountable and meets the expectation of another person Trustee’s antecedents to trust Trustor’s antecedent to trust PROPENSITY TO TRUST A general, not experience-based inclination to display faith and adopt a trusting attitude toward others
- 3. Big 5 model & Trust ICGSE'17
- 4. Why We Care About Trust • Trust fundamental to build “sense of teamness” • Lack of trust can mean: – Unwillingness to cooperate or share information – Perception of being on separate teams – Decreased goodwill towards others • The detrimental effects of lack of trust indirectly affect project performance (costs, speed, …) – Especially in case of distributed collaboration (no F2F interaction) ICGSE'17
- 5. Success Factors in Collaborative Development • ~13% of reviewed PRs in GitHub rejected for purely technical reasons Pull Request acceptance Contributor’s Experience & Social Connections • track record • connection strength • @mentions • # followers PRs technical characteristics • test cases • commit size • review comments + Gousios et al., ICSE’14, ICSE’15 Tsay et al., ICSE’14, FSE’14 Vasilescu et al., MSR’15 ICGSE'17
- 6. Research Question Trust ≈ Project performance • How does individual propensity to trust facilitate the successful collaborations in globally-distributed software projects? Successful collaboration ≈ Accepted Pull Request (PR) ICGSE'17
- 8. Study 6 integrators (20+ emails) 218 Pull Requests (Accepted / Not accepted) ~5K emails GH Torrent mlstats Tone Analyzer Propensity to Trust Scores (Low / High) Agreeableness ICGSE'17
- 9. • Logistic regression model – Outcome: PR acceptance – Predictor: Integrator’s propensity to trust Findings +34%Integrator with High propensity to trust ICGSE'17 Integrator with Low propensity to trust Control vars: # emails: # PRs reviewed
- 10. Limitations & Future work • Limited generalizability – 1 project – 6 integrators – 218 pull requests • Construct validity – Relied on agreeableness as a proxy of propensity to trust – Relied on IBM Watson Tone Analyzer tout court • Study replications on more projects with larger datasets • Exploit other personality theories (e.g., MMPI) • Replication with trust- specific linguistic resource (e.g., NRC emotion lexicon) ICGSE'17
- 11. Big 5 model & Trust ICGSE'17 Limitations & Future work • Limited generalizability – 1 project – 6 integrators – 218 pull requests • Construct validity – Relied on agreeableness as a proxy of propensity to trust – Relied on IBM Watson Tone Analyzer tout court • Study replications on more projects with larger datasets • Exploit other personality theories (e.g., MPPI) • Develop a trust-specific linguistic resource from SE corpora ICGSE'17 Study 6 integrators (20+ emails) 218 Pull Requests (Accepted / Not accepted) ~5K emails GH Torrent mlstats Tone Analyzer Propensity to Trust Scores (Low / High) agreeableness • Logistic regression model – Outcome: PR acceptance – Predictor: Integrator’s propensity to trust Findings 11 +34% Integrator with Low propensity to trust Integrator with High propensity to trust Thanks! @fcalefato
Editor's Notes
- Trust is a complex matter to study since it involves both interpersonal relationships (e.g., cultural issues between trustee and trustor) and facets of human behavior (e.g., personal traits). To date several definitions of trust have been given. A widely used and concise definition is provided by Jarvenpaa et al. “The expectations of one (the trustor) that the others (the trustees) will behave as predicted” [1] In other words, positive trust emerges when others’ actions meet our expectation; otherwise, negative trust, or mistrust, arises. When we assess someone’s trustworthiness, we do it along judging others along several dimensions or personal characteristics called antecedents of trust, that is, the properties of others that trigger the evaluation when one assessing their trustworthiness. Therefore, sharing these properties/antecedents about someone fosters trust building, although this is also influenced by one’s intrinsic propensity to trust, a natural disposition towards trusting others in general propensity to trust (i.e., (a dispositional willingness to rely on others) Trust propensity is a general disposition to trust people in life (this is a consumer trait that is relatively stable. People differ in this trait; some are always distrusting others, whereas others believe that people can be trusted. Disposition to trust is a general, i.e. not situation specific, inclination to display faith in humanity and to adopt a trusting stance toward others (Mayer et al. 1995; McKnight et al. 1998), and is perceived as a personality trait (Grabner-Kräuter et al., 2003). This tendency is not based upon experience with or knowledge of a specific trusted party (ref), but is the result of an ongoing lifelong experience and socialization. As an antecedent of trust, disposition to trust is most effective in initial phases of a relationship when the parties are still mostly unfamiliar with each other (Mayer et al. 1995) and before extensive ongoing relationships provide a necessary background for the formation of other trust-building beliefs, such as integrity, benevolence and ability. ability (e.g., skills, knowledge), benevolence (e.g., courtesy, availability), integrity relates to a set of moral norms and trustee’s characteristics usually considered as good as, for example, honesty, fairness, loyalty and discretion, and predictability (e.g., reliability, consistent behaviors) are the personal characteristics of a trustee that facilitate the establishment of the trust relationship with a trustor. More specifically, the ability and predictability dimensions are assessed by means of cognitive elaboration of personal and professional information. At the same time, affective-based appraisal leads to trust building along the dimensions of benevolence and integrity is adherence to a set of principles (such as study/work habits) thought to make the trustee dependable and reliable, according to the trustor
- 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.
- Why is trust so important in SE and to SE researchers?
- Connection strength = • prior interactions with core devs
- One common limitation identified in prior empirical-research findings is that there is no explicit measure of ‘how much’ improving trust contributes to a project performance. Difficult to measure trust, it is sensed self-reported data (read questionnaires) We seek for a more operative, quantitative measure, approximate trust by means of exchange of affective lexicon between a pair Performance is intuitively intended as a software project that delivers a quality product staying on budget and time schedule We approximate it as successful requests accepted PRs that cause the code base to grow, adding features, fixing bugs, one step closer to the project final goal
- You can look at the paper to check the formula with the coefficient estimates. For a given PR k, there’s a +34% increase in the chance of being merged when the propensity to trust score of the integrator shifts from Low to High.
- Big5 model: trust is just one facet of agreeableness Is it any reliable, we have seen with Sentiment Analysis tool how their lexicon is domain dependent. Minnesota Multiphasic Personality Inventory NRC lexicon