Arsonists or firefighters? Affectiveness in agile software development
1. Does the paper propose a new opinion mining approach?
No
2. Which opinion mining techniques are used (list all of them, clearly stating their name/reference)?
SentiStrength Emotion detector of Ortu et al. Ortu, M., Adams, B., Destefanis, G., Tourani, P., Marchesi, M., Tonelli, R.: Are bullies more productive? Empirical study of affectiveness vs. issue fixing time. In: Proceedings of the 12th Working Conference on Mining Software Repositories, MSR 2015
3. Which opinion mining approaches in the paper are publicly available? Write down their name and links. If no approach is publicly available, leave it blank or None.
SentiStrength http://sentistrength.wlv.ac.uk. As far as I understand the emotion detector is not available online
4. What is the main goal of the whole study?
to build Markov chain models which describe how developers interact in a distributed Agile environment evaluating politeness, sentiment and emotions
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
polarity of issue comments
6. Which dataset(s) the technique is applied on?
500K issue comments from open-source repositories from Ortu et al.: Ortu, M., Destefanis, G., Murgia, A., Marchesi, M., Tonelli, R., Adams, B.: The JIRA repository dataset: Understanding social aspects of software development. In: Proceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering, p. 1. ACM Projects are listed in Table 1
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
Full dataset used to be available at http://openscience.us/repo/social-analysis/social-aspects.html The page has been preserved on https://web.archive.org/web/20180808035130/http://openscience.us/repo/social-analysis/social-aspects.html but unfortunately the data is no longer available since terapromise is no longer available.
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
No, it is all about issues
9. Is the performance (precision, recall, run-time, etc.) of the technique verified? If yes, how did they verify it and what are the results?
No, not necessarily a problem since this is essentially the same data
10. Does the paper replicate the results of previous work? If yes, leave a summary of the findings (confirm/partially confirms/contradicts).
Not explicitly
11. What success metrics are used?
N/A
12. Write down any other comments/notes here.
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