Data Analytics for Optimized Matching in Software Development

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Muhammad Rezaul Karim",
  title =        "Data Analytics for Optimized Matching in Software
  school =       "Computer Science, University of Calgary",
  year =         "2017",
  address =      "Alberta, Canada",
  month =        oct # " 27",
  keywords =     "genetic algorithms, genetic programming, Artificial
  URL =          "",
  URL =          "",
  abstract =     "Decision-making in various forms of software
                 development is challenging, as the environment and
                 context where decisions are made is complex, uncertain
                 and/or dynamic. Because of the associated complexity,
                 decision making based on prior experience and gut
                 feelings often lead to sub-optimal decisions. Among the
                 various decision-making activities, stakeholders often
                 need to match one entity (e.g. software artefact, human
                 resource) with another (e.g. human resource, software
                 artifact). Data analytics has the potential to generate
                 insights, extract patterns and trends from data to
                 guide the decision makers to make better and informed
                 decisions under various complex decision scenarios
                 involving matching. To prove the benefits of data
                 analytic in matching, we have used five matching
                 decision problems from open source, closed-source and
                 crowd sourced software development context. First, with
                 the use of predictive analytics, we have shown how the
                 success and failure of crowd workers in a new task can
                 be predicted by learning patterns from their and their
                 competitors' past behaviours. Based on the predicted
                 success chance, we have also designed a task
                 recommendation system to prescribe best suited tasks to
                 crowd workers (task-worker matching). Second, by
                 integrating crowd workers' learning preference with
                 predictive analytics, we have demonstrated how task
                 recommendations can be generated from historical data
                 taking workers personal learning and earning goals into
                 account. The conducted user evaluation shows very
                 positive feedback about the usefulness of the
                 recommendations. Third, we have designed a theme
                 (semantic cohesiveness) based approach for
                 feature-release matching to prescribe features for the
                 next release of iterative and incremental software
                 development, considering multiple objectives,
                 constraints and stakeholders preference data. Fourth,
                 we have presented a multi-objective developer-bug
                 matching technique that can prescribe developers for a
                 batch of bugs balancing bug fix time and bug fix cost
                 using data mined from version control repository.
                 Finally, using textual data extracted from issue
                 tracking systems, we have proposed a collaborative
                 filtering and bi-term topic modelling based
                 recommendation system for tagging issues (tag-issue
                 matching). The conducted quantitative and qualitative
                 evaluation shows that data from various sources can be
                 used for effective matching in various forms of
                 software development.",
  notes =        "Embargoed until: 2018-02-23

                 Supervisor: Guenther


Genetic Programming entries for Muhammad Rezaul Karim