%0 Conference Paper %1 Karim:2016:ADA:2896839.2896845 %A Karim, Muhammad Rezaul %A Al Alam, S. M. Didar %A Kabeer, Shaikh Jeeshan %A Ruhe, Günther %A Baluta, Basil %A Mahmud, Shafquat %B Proceedings of the 4th International Workshop on Conducting Empirical Studies in Industry %C New York, NY, USA %D 2016 %I ACM %K service.support %P 7--13 %R 10.1145/2896839.2896845 %T Applying Data Analytics Towards Optimized Issue Management: An Industrial Case Study %U http://doi.acm.org/10.1145/2896839.2896845 %X This document describes our experience of applying data analytics at Plexina, a leading IT company working in the healthcare domain. The main goal of the project was to identify factors currently affecting issue management and to make analytics based suggestions for optimizing the process. Various statistical and machine learning techniques were applied on a data set extracted from six releases of Plexina, containing more than 666 issues. Statistical techniques successfully identified the various factors that leads to estimation inaccuracy related to issues as well as identified the hidden relationships existing among various variables. The employed predictive analytic models was also successful to some extent, in predicting effort estimation related inaccuracy associated with the issues. The insights provided by the entire data analytics study can be of great help to product managers or the developers to make more informed decisions. In addition, the guidelines presented in this paper based on the lessons learnt can be applied to other data analytics and academia-industry collaboration project. %@ 978-1-4503-4154-7

@inproceedings{Karim:2016:ADA:2896839.2896845, abstract = {This document describes our experience of applying data analytics at Plexina, a leading IT company working in the healthcare domain. The main goal of the project was to identify factors currently affecting issue management and to make analytics based suggestions for optimizing the process. Various statistical and machine learning techniques were applied on a data set extracted from six releases of Plexina, containing more than 666 issues. Statistical techniques successfully identified the various factors that leads to estimation inaccuracy related to issues as well as identified the hidden relationships existing among various variables. The employed predictive analytic models was also successful to some extent, in predicting effort estimation related inaccuracy associated with the issues. The insights provided by the entire data analytics study can be of great help to product managers or the developers to make more informed decisions. In addition, the guidelines presented in this paper based on the lessons learnt can be applied to other data analytics and academia-industry collaboration project.}, acmid = {2896845}, added-at = {2016-08-19T17:00:50.000+0200}, address = {New York, NY, USA}, author = {Karim, Muhammad Rezaul and Al Alam, S. M. Didar and Kabeer, Shaikh Jeeshan and Ruhe, G\"{u}nther and Baluta, Basil and Mahmud, Shafquat}, biburl = {https://www.bibsonomy.org/bibtex/2b34b968f39a9de1445e79edb7b6c98f8/ispma}, booktitle = {Proceedings of the 4th International Workshop on Conducting Empirical Studies in Industry}, doi = {10.1145/2896839.2896845}, interhash = {a905dd7338423ed75c83f3e5089d9d05}, intrahash = {b34b968f39a9de1445e79edb7b6c98f8}, isbn = {978-1-4503-4154-7}, keywords = {service.support}, location = {Austin, Texas}, numpages = {7}, pages = {7--13}, publisher = {ACM}, series = {CESI '16}, timestamp = {2016-08-19T17:00:50.000+0200}, title = {Applying Data Analytics Towards Optimized Issue Management: An Industrial Case Study}, url = {http://doi.acm.org/10.1145/2896839.2896845}, year = 2016 }