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Software programmer management: a machine learning and human computer interaction framework for optimal task assignment

Published: 11 November 2014 Publication History

Abstract

This paper attempts optimal task assignment at the enterprise-level by assigning complexity metrics to the programming tasks and predicting task completion times for each of these tasks based on a machine learning framework that factors in programmer attributes. The framework also considers real-time programmer state by using a simple EEG device to detect programmer mood. A final task assignment is made using a PDTS solver.

References

[1]
Krishna P B et al. A Scheduling Algorithm for Parallelizable Dependent Tasks. Proceedings of the International Parallel Processing Symposium, 1991. Figure 1. PDTS Considered for Optimization.
[2]
J Cardoso et al. A Discourse on Complexity of Process Models. BPM Workshop, 2006.
[3]
F B Bastani. An approach to measuring program complexity. COMPSAC 1983.
[4]
S N Cant and B Henderson-Sellers. A conceptual model of cognitive complexity of elements of the programming process. Information and Software Technology, 37(7).
[5]
Capers J, Applied Software Measurement: Global Analysis of Productivity and Quality, McGraw-Hill Professional, New York, 2008.
[6]
A Marcus et al. Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems. IEEE Transactions on Software Engineering, 2008.
[7]
M Anthony et al. Coupling Metrics for Ontology-Based Systems. IEEE Software, 2006.
[8]
S Yu and S Zhou. A Survey on Metrics of Software Complexity, 2010.
[9]
H Stanislaw et al. A note on the quantification of computer programming skill. International Journal of Human-Computer Studies, Vol. 41, 1994.
[10]
G Braught et al. The effects of pair-programming on individual programming skill. Proceedings of the 39th SIGCSE technical symposium on Computer science education, 2008.
[11]
C Daly and J Waldron. Assessing the assessment of programming ability. Proceedings of the 35th SIGCSE technical symposium on Computer science education, 2004.
[12]
B Curtis et al. Third time charm: Stronger prediction of programmer performance by software complexity metrics. Proceedings of the 4th international conference on Software engineering, 1979.
[13]
IA Khan et al. Programmer's mood and their performance. Proceedings of the 13th Eurpoean conference on Cognitive ergonomics: trust and control in complex socio-technical systems, 2006.
[14]
G Dahl et al. Improving deep neural networks for LVCSR using rectified linear units and dropout. IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
[15]
L Rutkowski and M Jaworski. Decision Trees for Mining Data Streams Based on the Gaussian Approximation. IEEE Transactions on Knowledge and Data Engineering, 2014.

Cited By

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  • (2020)Design of Programmer’s Skill Evaluation Metrics for Effective Team SelectionWireless Personal Communications: An International Journal10.1007/s11277-020-07517-6114:4(3049-3080)Online publication date: 1-Oct-2020

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cover image ACM Conferences
FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering
November 2014
856 pages
ISBN:9781450330565
DOI:10.1145/2635868
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 November 2014

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Author Tags

  1. Complexity metrics
  2. completion time prediction
  3. task assignment

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Overall Acceptance Rate 17 of 128 submissions, 13%

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View all
  • (2020)Design of Programmer’s Skill Evaluation Metrics for Effective Team SelectionWireless Personal Communications: An International Journal10.1007/s11277-020-07517-6114:4(3049-3080)Online publication date: 1-Oct-2020

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