Heuristic Correlative Features and Least Square Multi-Layer Perception on Software Process Improvement

Authors(1) :-Dr. A. Saranya

Successful software process in its own right not only has various favorable inferences for the software industry, but also the broad stakeholder group.As several software processes exists, it becomes difficult for the project managers to select optimal software process model from available software processes. Improper selection of software process not only increases the software development life cycle time, but also reduces the success rate. Therefore, it is necessary to introduce new and efficient technique to reduce the software development life cycle time with minimum user effort. In this paper, a new attribute based recommendation and machine learning technique called, Heuristic Correlative Features and Least Square Multi Layer Perceptron (HCF-LSMLP) is proposed for helping the project managers to select the most suitable software projects among the existing software projects. This technique introduces a heuristic correlation based feature selection that reduces the software process development cycle time by constructing attributes based on top n recommendations without increasing the computational complexity. Moreover, the proposed HCF-LSMLP technique for suitable software project selection provides better performance in terms of true positive rate than the other existing techniques. Besides, the error on the software process selection is also tackled using the Least Square method in an efficient manner by minimizing the sum of squared residuals. The main advantage of the proposed technique is that it minimizes the software development life cycle time and improves the true positive rate.

Authors and Affiliations

Dr. A. Saranya
Assistant Professor, Department of Computer Application V.V.V College for Women, Virudunagar, Tamil Nadu, India

Software Process, Attribute Based Recommendation, Machine Learning, Heuristic Correlative Features, Least Square, Multi Layer Perceptron

  1. George A. Sielis, Aimilia Tzanavari and George A. Papadopoulos, "ArchReco: a software tool to assist software design based on context aware recommendations of design patterns", Journal of Software Engineering Research and Development, Springer, May 2017
  2. Mujtaba Alshakhouri, Jim Buchan, Stephen G. MacDonell, "Synchronised visualisation of software process and product artefacts: Concept, design and prototype implementation", Information and Software Technology, Elsevier, Jan 2018
  3. Suryanto Nugroho, Sigit Hadi Waluyo, Luqman Hakim, "Comparative Analysis of Software Development Methods between Parallel, V-Shaped and Iterative", International Journal of Computer Applications (0975 – 8887) Volume 169 – No.11, July 2017
  4. D. Peteiro-Barral, V. Bolón-Canedo, A. Alonso-Betanzos, B. Guijarro-Berdiñas, N. Sánchez-Maroño, "Toward the scalability of neural networks through feature selection", Expert Systems with Applications, Elsevier, June 2012
  5. Delroy A. Chevers, Annette M. Mills, Evan W. Duggan & Stanford E. Moore, "Toward a Simplified Software ProcessImprovement Framework for Small SoftwareDevelopment Organizations", Journal of Global Information Technology Management, Taylor and Francis Group, June 2017
  6. Federica Sarro, Filomena Ferrucciy, Mark Harman, Alessandra Mannay, Jian Ren, "Adaptive Multi-objective Evolutionary Algorithmsfor Overtime Planning in Software Projects", IEEE Transactions on Software Engineering, Volume 43, Issue 10, Oct. 1 2017
  7. Rashina Hoda, James Noble, and Stuart Marshall, "Self-Organizing Roles onAgile Software Development Teams", IEEE Transactions on Software Engineering, VOL. 39, NO. 3, MARCH 2013
  8. Tracy Hall, Sarah Beecham, David Bowes, David Gray, and Steve Counsell, "A Systematic Literature Review onFault Prediction Performancein Software Engineering", IEEE Transactions on Software Engineering, VOL 38, NO 6, NOVEMBER/DECEMBER 2012
  9. Michael Unterkalmsteiner, Tony Gorschek, A.K.M. Moinul Islam, Chow Kian Cheng, Rahadian Bayu Permadi, and Robert Feldt, "Evaluation and Measurementof Software Process Improvement—A Systematic Literature Review", IEEE Transactions on Software Engineering, VOL. 38, NO. 2, MARCH/APRIL 2012
  10. Martin Shepperd, David Bowes, and Tracy Hall, "Researcher Bias: The Use of Machine Learningin Software Defect Prediction", IEEE Transactions on Software Engineering, VOL. 40, NO. 6, JUNE 2014
  11. Patrick Rempel and Parick Mader, "Preventing Defects: The Impact of RequirementsTraceability Completeness on Software Quality", IEEE Transactions on Software Engineering (Volume: 43, Issue: 8, Aug. 1 2017)
  12. Neha Gehlot and Jagdeep Kaur, "Dynamic inheritance coupling metric-design andanalysis for assessing reusability", Int. J. Software Engineering, Technology and Applications, Vol. 1, No. 1, 2015
  13. Marco Kuhrmann, Philipp Diebold and Jürgen Münch, "Software process improvement: asystematic mapping study on the stateof the art", Peer J Computer Science, May 2016
  14. Mushtaq RazaJoão Pascoal Faria, Luis AmaroPedro Castro Henriques, "WebProcessPAIR: Recommendation System for Software ProcessImprovement", ACM, July 2017
  15. Arif Ali Khan, Jacky Keung,Shahid Hussain, Mahmood Niazi, Muhammad Manzoor IlahiTamimy, "Understanding Software Process Improvement in GlobalSoftware Development: A Theoretical Framework ofHuman Factors", ACM Symposium on AppliedComputing, Copyright 2017
  16. Qiuying Li, Hoang Pham, "A testing-coverage software reliability modelconsidering fault removal efficiency and errorgeneration", PLOS ONE | https://doi.org/10.1371/journal.pone.0181524 July 27, 2017
  17. Mahmood Niazi, "A comparative study of software process improvementimplementation success factors", John Wiley & Sons Limited, Aug 2015
  18. Jung-Chieh Lee, Yih-Chearng Shiue, Chung-Yang Chen, "Examining the impacts of organizational culture and top management support of knowledge sharing on the success of software process improvement", Computers in Human Behavior, Elsevier, Sep 2015
  19. Fernanda Grazioli, Diego Vallespir, Leticia Pérez, and SilvanaMoreno, "The Impact of the PSP on Software Quality: Eliminatingthe Learning Effect Threat through a Controlled Experiment", Hindawi Publishing CorporationAdvances in Software EngineeringVolume 2014
  20. K. Karnavel and R. Dillibabu, "Development and Application of New Quality Model forSoftware Projects", Hindawi Publishing Corporation, The Scientific World JournalVolume 2014

Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 40-45
Manuscript Number : CSEIT18375
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. A. Saranya, "Heuristic Correlative Features and Least Square Multi-Layer Perception on Software Process Improvement", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.40-45, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT18375

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