CSU33012-202122 SOFTWARE ENGINEERING

The report should have four sections.

The first section should be about how one can measure engineering activity. For example, one could look at the structure of code and calculate its complexity. Another example, one could look at the frequency of code commits to try to decide on how productive a software engineer is. These kinds of measures and others form the basis of how software engineering activity can be measured. Your first section should explore and report your findings on this.

The second section should be concerned with the platforms on which one can gather and perform calculations over these data sets. The important infrastructure here is the emergence of utility computering which supports the gathering of large volumes of data, and the processing of this data via algorithms. This infrastructure enables complex and computationally expensive calculations to be performed. Your text in this section should explore what is possible here, and also what specialist infrastructure has emerged to perform various kinds of data analysis.

The third section of your report should be concerned with various kinds of computation that could be done over software engineering data, in order to profile the performance of software engineers. And there are various techniques for data computation, including but not limited to defeasible argumentation of the kind used in expert systems, simple counting, various software engineering algorithms measuring concepts such as coal and complexity, and various machine learning approaches for plastering and analysing data sets. Your section should attempt to set out the landscape here and consider the strengths and weaknesses of various techniques for the problem at hand.

Before section should be concerned with the ethics and legal or moral issues surrounding the processing of this kind of personal data. The core question is whether it is reasonable to perform various kinds of analysis on the performance of software engineers as they go about their work, or whether some of this crosses a line. And your goal here should be to form an opinion that is informed by your prior research and assessment. You may take any view that you like.

Reading List

The following reading list is a selection of scholarly papers, books, and online material dealing broadly with the issue of measuring workers in order to assess and enhance performance, and in some cases augment or replace with automated systems. The material is in the main focussed on the monitoring of software engineers but the broad implications concern most if not all professional collaborative work. Use it as a starting point for your exploration of the subject.

https://www.youtube.com/watch?v=Dp5_1QPLps0

http://www.citeulike.org/group/3370/article/12458067

http://2016.msrconf.org/

http://www.nextlearning.nl/wp-content/uploads/sites/11/2015/02/McKinsey-on-Impact-social-technologies.pdf

http://s3.amazonaws.com/academia.edu.documents/35963610/2014_American_Behavioral_Scientist-2014-Chen-0002764214556808_networked_worker.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1479122144&Signature=pO6ZkNbSZgbNTqHMd7xyIdMV5iE%3D&response-content-disposition=inline%3B%20filename%3D2014_Do_Networked_Workers_Have_More_Con.pdf

https://www.researchgate.net/profile/Jan_Sauermann2/publication/285356496_Network_Effects_on_Worker_Productivity/links/565d91c508ae4988a7bc7397.pdf

http://www.hitachi.com/rev/pdf/2015/r2015_08_116.pdf

http://patentimages.storage.googleapis.com/pdfs/US20130275187.pdf

Fenton, N. E., and Martin, N. (1999) "Software metrics: successes, failures and new directions." Journal of Systems and Software 47.2 pp. 149-157.

Stephen H. Kan. 2002. Metrics and Models in Software Quality Engineering (2nd ed.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.

Grambow, G., Oberhauser, R. and Reichert, M (2013) Automated Software Engineering Process Assessment: Supporting Diverse Models using an Ontology. Int'l Journal on Advances in Software, 6 (1 & 2). pp. 213-224.

Dittrich, Andrew, Mehmet Hadi Gunes, and Sergiu Dascalu. (2013) "Network Analysis of Software Repositories: Identifying Subject Matter Experts." Complex Networks. Springer Berlin Heidelberg. pp. 187-198.

W. Pan, W. Dong, M. Cebrian, T. Kim, J. H. Fowler and A. S. Pentland, "Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems," in IEEE Signal Processing Magazine, vol. 29, no. 2, pp. 77-86, March 2012.

P.M. Johnson et al., “Beyond the Personal Software Process: Metrics Collection and Analysis for the Differently Disciplined,” Proc. 25th Int’l Conf. Software Eng. (ICSE 03), IEEE CS, 2003, pp. 641–646.

Johnson, Philip M., and Hongbing Kou. "Automated recognition of test-driven development with Zorro." Agile Conference (AGILE), 2007. IEEE, 2007.

Johnson, Philip M., et al. "Improving software development management through software project telemetry." IEEE software 22.4 (2005): 76-85.

Grambow, G., Oberhauser, R. and Reichert, M. (2013) Automated Software Engineering Process Assessment: Supporting Diverse Models using an Ontology. Int'l Journal on Advances in Software, 6 (1 & 2). pp. 213-224.

Taghi Javdani , Hazura Zulzalil, Abd. Azim Abd. Ghani, Abubakar Md. Sultan, On the current measurement practices in agile software development, International Journal of Computer Science Issues, 2012, Vol. 9, Issue 4, No. 3, pp. 127-133.

W. Snipes, V. Augustine, A. R. Nair and E. Murphy-Hill, "Towards recognizing and rewarding efficient developer work patterns," 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, 2013, pp. 1277-1280.

Martin P. Robillard, Wesley Coelho, and Gail C. Murphy. 2004. How Effective Developers Investigate Source Code: An Exploratory Study. IEEE Trans. Softw. Eng. 30, 12 (December 2004), 889-903.

G. C. Murphy, M. Kersten, and L. Findlater, “How are Java software developers using the Elipse IDE?” IEEE Software, vol. 23, no. 4, pp. 76–83, Jul. 2006.

P. M. Johnson, H. Kou, J. Agustin, C. Chan, C. Moore, J. Miglani, S. Zhen, and W. E. J. Doane, “Beyond the personal software process: Metrics collection and analysis for the differently disciplined,” in Proceedings of the 25th international Conference on Software Engineering. IEEE Computer Society, 2003.

A. Sillitti, A. Janes, G. Succi, and T. Vernazza, “Collecting, integrating and analyzing software metrics and personal software process data,” in Proceedings of the 29th Euromicro Conference. IEEE, 2003, pp. 336– 342.

E. B. Passos, D. B. Medeiros, P. A. S. Neto and E. W. G. Clua, (2011) "Turning Real-World Software Development into a Game," Games and Digital Entertainment (SBGAMES), 2011 Brazilian Symposium on, Salvador, 2011, pp. 260-269.,

L. Singer and K. Schneider, "It was a bit of a race: Gamification of version control," Games and Software Engineering (GAS), 2012 2nd International Workshop on, Zurich, 2012, pp. 5-8.

Silverman, Rachel (Nov 2, 2011). "Latest Game Theory: Mixing Work and Play — Companies Adopt gaming Techniques to Motivate Employees". Wallstreet Journal.

Hassan, A.E. and T. Xie, Software intelligence: the future of mining software engineering data, in Proceedings of the FSE/SDP workshop on Future of software engineering research2010, ACM: Santa Fe, New Mexico, USA. p. 161-166.

Di Penta, M. (2012) Mining developers' communication to assess software quality: Promises, challenges, perils. In Emerging Trends in Software Metrics (WETSoM), 2012 3rd International Workshop on. IEEE.

R. Want, A. Hopper, a. Veronica Falc and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10(1):91–102, 1992.

Pentland, A. (2014). Social physics: how good ideas spread-the lessons from a new science. Penguin.

E. Murphy-Hill and G. C. Murphy, “Peer interaction effectively, yet infrequently, enables programmers to discover new tools,” in Proceedings of the ACM 2011 Conference on Computer supported cooperative work, CSCW ’11. ACM, 2011, pp. 405–414.