http://iet.metastore.ingenta.com
1887

Threats to validity in search-based predictive modelling for software engineering

Threats to validity in search-based predictive modelling for software engineering

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Software — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A number of studies in the literature have developed effective models to address prediction tasks related to a software product such as estimating its development effort, or its change/defect proneness. These predictions are critical as they help in identifying weak areas of a software product and thus guide software project managers in effective allocation of project resources to these weak parts. Such practices assure good quality software products. Recently, the use of search-based approaches (SBAs) for developing software prediction models (SPMs) has been successfully explored by a number of researchers. However, in order to develop effective and practical SPMs it is imperative to analyse various sources of threats. This study extensively reviews 93 primary studies, which use SBAs for developing SPMs of four commonly used software attributes (effort, defect-proneness, maintainability and change-proneness) in order to discuss and identify the various sources of threats while using these approaches for SPMs. The study also lists various actions that may be taken in order to minimise these threats. Furthermore, best practice examples in literature and the year-wise trends of threats indicating the most common threats missed by researchers are provided to help academicians and practitioners in designing effective studies for developing SPMs using SBAs.

References

    1. 1)
      • 1. Malhotra, R., Khanna, M., Raje, R.R.: ‘On the application of search-based techniques for software engineering predictive modeling: a systematic review and future directions’, Swarm Evol. Comput., 2017, 32, pp. 85109.
    2. 2)
      • 2. Harman, M., Jones, B.F.: ‘Search-based software engineering’, Inf. Softw. Technol., 2001, 43, (14), pp. 833839.
    3. 3)
      • 3. Harman, M.: ‘The relationship between search based software engineering and predictive modeling’. Proc. Int. Conf. on Predictive Models in Software Engineering, Timisoara, Romania, 2010, p. 1.
    4. 4)
      • 4. Harman, M., McMinn, P., De Souza, J.T., et al: ‘Search based software engineering: techniques, taxonomy, tutorial’. Proc. Empirical Software Engineering and Verification, Elba Island, Italy, 2012, pp. 159.
    5. 5)
      • 5. Harman, M., Mansouri, S.A., Zhang, Y.: ‘Search-based software engineering: trends, techniques and applications’, ACM Comput. Surv., 2012, 45, (11), p. 1.
    6. 6)
      • 6. De Oliveira Barros, M., Dias-Neto, A.C.: ‘Threats to validity in search-based software engineering empirical studies’. RelaTe-DIA. 2011, 5, (1), UNIRIO-Universidade Federal do Estado do Rio de Janeiro, Tech. Rep. TR 0006/2011.
    7. 7)
      • 7. Malhotra, R., Khanna, M.: ‘Common threats to software quality predictive modeling studies using search-based techniques’. Proc. Int. Conf. on Advances in Computing, Communications and Informatics, Jaipur, India, 2016, pp. 568574.
    8. 8)
      • 8. Cook, T.D., Campbell, D.T., Day, A.: ‘Quasi-experimentation: design & analysis issues for field settings’ (Houghton Mifflin, Boston, 1979).
    9. 9)
      • 9. Malhotra, R.: ‘Empirical research in software engineering: concepts, analysis, and applications’ (CRC Press, Taylor & Francis, 2016).
    10. 10)
      • 10. Neto, A.A., Conte, T.: ‘A conceptual model to address threats to validity in controlled experiments’. Proc. Int. Conf. on Evaluation and Assessment in Software Engineering, Porto de Galinhas, Brazil, 2013, pp. 8285.
    11. 11)
      • 11. Kitchenham, B.A., Budgen, D., Brereton, P.: ‘Evidence-based software engineering and systematic reviews’ (CRC Press, Chapman & Hall, 2016).
    12. 12)
      • 12. Xanthakis, S., Ellis, C., Skourlas, C., et al: ‘Application of genetic algorithms to software testing’. Proc. Int. Conf. on Software Engineering and Applications, Toulouse, France, 1992, pp. 625636.
    13. 13)
      • 13. Briand, L.C., Wust, J.: ‘Empirical studies of quality models in object-oriented systems’, Adv. Comput., 2002, 56, pp. 97166.
    14. 14)
      • 14. Corazza, A., Di Martino, S., Ferrucci, F., et al: ‘Using tabu search to configure support vector regression for effort estimation’, Empir. Softw. Eng., 2013, 18, (3), pp. 506546.
    15. 15)
      • 15. Xia, X., Lo, D., Pan, S.J., et al: ‘Hydra: massively compositional model for cross-project defect prediction’, IEEE Trans. Softw. Eng., 2016, 42, (10), pp. 977998.
    16. 16)
      • 16. Ferrucci, F., Salza, P., Sarro, F: ‘Using hadoop MapReduce for parallel genetic algorithms: a comparison of the global, grid and island models’, Evol. Comput., 2017, pp. 133.
    17. 17)
      • 17. Sarro, F., Petrozziello, A., Harman, M.: ‘Multi-objective software effort estimation’. Proc. Int. Conf. Software Engineering, Austin, Texas, United States, 2016, pp. 619630.
    18. 18)
      • 18. Malhotra, R., Khanna, M.: ‘An exploratory study for software change prediction in object-oriented systems using hybridized techniques’, Autom. Softw. Eng., 2017, 24, (3), pp. 673717.
    19. 19)
      • 19. Hosseini, S., Turhan, B., Mäntylä, M.: ‘A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction’, Inf. Softw. Technol., 2018, 95, pp. 296312.
    20. 20)
      • 20. De Carvalho, A.B., Pozo, A., Vergilio, S.R.: ‘A symbolic fault-prediction model based on multiobjective particle swarm optimization’, J. Syst. Softw., 2010, 83, (5), pp. 868882.
    21. 21)
      • 21. Canfora, G., De Lucia, A., Di Penta, M., et al: ‘Multi-objective cross-project defect prediction’. Proc. Int. Conf. Software Testing, Verification and Validation, Luxembourg, 2013, pp. 252261.
    22. 22)
      • 22. Abdi, Y., Parsa, S., Seyfari, Y.: ‘A hybrid one-class rule learning approach based on swarm intelligence for software fault prediction’, Innov. Syst. Softw. Eng., 2015, 11, (4), pp. 289301.
    23. 23)
      • 23. Murillo-Morera, J., Quesada-López, C., Castro-Herrera, C., et al: ‘A genetic algorithm based framework for software effort prediction’, J. Softw. Eng. Res. Dev., 2017, 5, (1), p. 4.
    24. 24)
      • 24. Ferrucci, F., Gravino, C., Oliveto, R., et al: ‘Using tabu search to estimate software development effort’. Int. Workshop on Software Measurement, Amsterdam, The Netherlands, 2009, pp. 307320.
    25. 25)
      • 25. Oliveira, A.L., Braga, P.L., Lima, R.M., et al: ‘GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation’, Inf. Softw. Technol., 2010, 52, (11), pp. 11551166.
    26. 26)
      • 26. Bardsiri, V.K., Jawawi, D.N., Hashim, S.Z., et al: ‘A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons’, Empir. Softw. Eng., 2014, 19, (4), pp. 857884.
    27. 27)
      • 27. Arar, Ö.F., Ayan, K.: ‘Software defect prediction using cost-sensitive neural network’, Appl. Soft Comput., 2015, 33, pp. 263277.
    28. 28)
      • 28. Afzal, W., Torkar, R.: ‘Towards benchmarking feature subset selection methods for software fault prediction’, in Pedrycz, W., Succi, G., Sillitti, A. (Eds.): ‘Computational intelligence and quantitative software engineering’ (Springer, Cham, 2016), pp. 3358.
    29. 29)
      • 29. Benala, T.R., Mall, R.: ‘DABE: differential evolution in analogy-based software development effort estimation’, Swarm Evol. Comput., 2018, 38, pp. 158172.
    30. 30)
      • 30. Catal, C., Diri, B.: ‘Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem’, Inf. Sci., 2009, 179, (8), pp. 10401058.
    31. 31)
      • 31. Ferrucci, F., Gravino, C., Oliveto, R., et al: ‘Investigating tabu search for web effort estimation’. Proc. Conf. Software Engineering and Advanced Applications, Lille, France, 2010, pp. 350357.
    32. 32)
      • 32. Barros, R.C., Basgalupp, M.P., Cerri, R., et al: ‘A grammatical evolution approach for software effort estimation’. Proc. Conf. Genetic and Evolutionary Computation, Amsterdam, The Netherlands, 2013, pp. 14131420.
    33. 33)
      • 33. Minku, L.L.., Yao, X.: ‘Software effort estimation as a multiobjective learning problem’, ACM Trans. Softw. Eng. Methodol., 2013, 22, (4), p. 35.
    34. 34)
      • 34. Jin, C., Jin, S.W.: ‘Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization’, Appl. Soft Comput., 2015, 35, pp. 717725.
    35. 35)
      • 35. Ryu, D., Baik, J.: ‘Effective multi-objective naïve Bayes learning for cross-project defect prediction’, Appl. Soft Comput., 2016, 49, pp. 10621077.
    36. 36)
      • 36. Mauša, G., Grbac, T.G.: ‘Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study’, Appl. Soft Comput., 2017, 55, pp. 331351.
    37. 37)
      • 37. Hochman, R., Khoshgoftaar, T.M., Allen, E.B., et al: ‘Evolutionary neural networks: a robust approach to software reliability problems’. Proc. Int. Symp. Software Reliability Engineering, Albuquerque, NM, USA, 1997, pp. 1326.
    38. 38)
      • 38. Ferrucci, F., Gravino, C., Oliveto, R., et al: ‘Genetic programming for effort estimation: an analysis of the impact of different fitness functions’. Proc. Int. Symp. Search Based Software Engineering, Benevento, Italy, 2010, pp. 8998.
    39. 39)
      • 39. Afzal, W.: ‘Using faults-slip-through metric as a predictor of fault-proneness’. Proc. Asia Pacific Software Engineering Conf. (APSEC), Sydney, Australia, 2010, pp. 414422.
    40. 40)
      • 40. Sarro, F., Di Martino, S., Ferrucci, F., et al: ‘A further analysis on the use of genetic algorithm to configure support vector machines for inter-release fault prediction’. Proc. ACM Symp. on Applied Computing, Trento, Italy, 2012, pp. 12151220.
    41. 41)
      • 41. Malhotra, R., Khanna, M.: ‘Analyzing software change in open-source projects using artificial immune systems algorithms’. Proc. Int. Conf. Advances in Computing, Communications and Informatics, Noida, India, 2014, pp. 26742680.
    42. 42)
      • 42. Bansal, A.: ‘Empirical analysis of search-based algorithms to identify change prone classes of open-source software’, Comput. Lang., Syst. Struct., 2017, 47, (2), pp. 211231.
    43. 43)
      • 43. Dolado, J.J.: ‘A validation of the component-based method for software size estimation’, IEEE Trans. Softw. Eng., 2000, 26, (10), pp. 10061021.
    44. 44)
      • 44. Burgess, C.J., Lefley, M.: ‘Can genetic programming improve software effort estimation? A comparative evaluation’, Inf. Softw. Technol., 2001, 43, (14), pp. 863873.
    45. 45)
      • 45. Liu, Y., Khoshgoftaar, T.M., Seliya, N.: ‘Evolutionary optimization of software quality modeling with multiple repositories’, IEEE Trans. Softw. Eng., 2010, 36, (6), pp. 852864.
    46. 46)
      • 46. Azar, D.: ‘A genetic algorithm for improving accuracy of software quality predictive models: a search-based software engineering approach’, Int. J. Comput. Intell. Appl., 2010, 9, (02), pp. 125136.
    47. 47)
      • 47. Sarro, F., Ferrucci, F., Gravino, C.: ‘Single and multiobjective genetic programming for software development effort estimation’. Proc. ACM Symp. on Applied Computing, Trento, Italy, 2012, pp. 12211226.
    48. 48)
      • 48. Minku, L.L., Yao, X.: ‘An analysis of multi-objective evolutionary algorithms for training ensemble models based on different performance measures in software effort estimation’. Proc. Int. Conf. Predictive Models in Software Engineering, San Francisco, CA, USA, 2013, p. 8.
    49. 49)
      • 49. Malhotra, R.: ‘Comparative analysis of statistical and machine learning methods for predicting faulty modules’, Appl. Soft Comput., 2014, 21, pp. 286297.
    50. 50)
      • 50. Malhotra, R., Khanna, M.: ‘A new metric for predicting software change using gene expression programming’. Proc. Int. Workshop on Emerging Trends in Software Metrics, Hyderabad, India, 2014, pp. 814.
    51. 51)
      • 51. Shukla, K.K.: ‘Neuro-genetic prediction of software development effort’, Inf. Softw. Technol., 2000, 42, (10), pp. 701713.
    52. 52)
      • 52. Liu, Y., Khoshgoftaar, T.M.: ‘Genetic programming model for software quality classification’. Proc. Int. Symp. High Assurance Systems Engineering, Boco Raton, FL, USA, 2001, pp. 127136.
    53. 53)
      • 53. Kirsopp, C., Shepperd, M., Hart, J.: ‘Search heuristics, case-based reasoning and software project effort prediction’. Proc. Conf. on Genetic and Evolutionary Computation, New York, USA, 2002, pp. 13671374.
    54. 54)
      • 54. Shan, Y., McKay, R.I., Lokan, C.J., et al: ‘Software project effort estimation using genetic programming’. Proc. Int. Conf. Communications, Circuits and Systems and West Sino Expositions, Chengdu, China, 2002, vol. 2, pp. 11081112.
    55. 55)
      • 55. Ferrucci, F., Gravino, C., Oliveto, R., et al: ‘Estimating software development effort using tabu search’. Proc. Int. Conf. Enterprise Information Systems, Madeira, Portugal, 2010, pp. 236241.
    56. 56)
      • 56. Bardsiri, V.K., Jawawi, D.N., Hashim, S.Z., et al: ‘A PSO-based model to increase the accuracy of software development effort estimation’, Softw. Qual. J., 2013, 21, (3), pp. 501526.
    57. 57)
      • 57. Basgalupp, M.P., Barros, R.C., Da Silva, T.S., et al: ‘Software effort prediction: a hyper-heuristic decision-tree based approach’. Proc. Annual ACM Symp. on Applied Computing, Coimbra, Portugal, 2013, pp. 11091116.
    58. 58)
      • 58. Harman, M., Islam, S., Jia, Y., et al: ‘Less is more: temporal fault predictive performance over multiple hadoop releases’. Proc. Int. Symp. on Search Based Software Engineering, Fortaleza, Brazil, 2014, pp. 240246.
    59. 59)
      • 59. Lefley, M., Shepperd, M.J: ‘Using genetic programming to improve software effort estimation based on general data sets’. Proc. Conf. Genetic and Evolutionary Computation, Chicago, Illinois, USA, 2003, pp. 24772487.
    60. 60)
      • 60. Khoshgoftaar, T.M., Seliya, N., Liu, Y.: ‘Genetic programming-based decision trees for software quality classification’. Proc. Int. Conf. Tools with Artificial Intelligence, Sacramento, California, USA, 2003, pp. 374383.
    61. 61)
      • 61. Huang, S.J., Chiu, N.H.: ‘Optimization of analogy weights by genetic algorithm for software effort estimation’, Inf. Softw. Technol., 2006, 48, (11), pp. 10341045.
    62. 62)
      • 62. Chiu, N.H., Huang, S.J.: ‘The adjusted analogy-based software effort estimation based on similarity distances’, J. Syst. Softw., 2007, 80, (4), pp. 628640.
    63. 63)
      • 63. Vandecruys, O., Martens, D., Baesens, B., et al: ‘Mining software repositories for comprehensible software fault prediction models’, J. Syst. Softw., 2008, 81, (5), pp. 823839.
    64. 64)
      • 64. Tsakonas, A., Dounias, G.: ‘Deriving models for software project effort estimation by means of genetic programming’. Proc. Int. Conf. Knowledge Discovery and Information Retreival, Madeira, Portugal, 2009.
    65. 65)
      • 65. Azar, D., Vybihal, J.: ‘An ant colony optimization algorithm to improve software quality prediction models: case of class stability’, Inf. Softw. Technol., 2011, 53, (4), pp. 388393.
    66. 66)
      • 66. Chavoya, A., Lopez-Martin, C., Meda-Campa, M.E.: ‘Applying genetic programming for estimating software development effort of short-scale projects’. Proc. Int. Conf. Information Technology: New Generations, Lasvegas, Nevada, USA, 2011, pp. 174179.
    67. 67)
      • 67. Basgalupp, M.P., Barros, R.C., Ruiz, D.D.: ‘Predicting software maintenance effort through evolutionary-based decision trees’. Proc. ACM Symp. on Applied Computing, Trento, Italy, 2012, pp. 12091214.
    68. 68)
      • 68. Yu, L.: ‘An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining’, Inf. Sci., 2012, 191, pp. 3146.
    69. 69)
      • 69. Abaei, G., Selamat, A.: ‘A survey on software fault detection based on different prediction approaches’, Vietnam J. Comput. Sci., 2014, 1, (2), pp. 7995.
    70. 70)
      • 70. Malhotra, R., Khanna, M.: ‘Mining the impact of object-oriented metrics for change prediction using machine learning and search-based techniques’. Proc. Int. Conf. Advances in Computing, Communications and Informatics, Kochi, Kerela, 2015, pp. 228234.
    71. 71)
      • 71. Dolado, J.J., Fernandez, L.: ‘Genetic programming, neural networks and linear regression in software project estimation’. Proc. Int. Conf. on Software Process Improvement, Research, Education and Training, London, Britain, 1998, pp. 157171.
    72. 72)
      • 72. Regolin, E.N., De Souza, G.A., Pozo, A.R., et al: ‘Exploring machine learning techniques for software size estimation’. Proc. Int. Conf. Chilean Computer Science Society, Cautin, Chile, 2003, pp. 130136.
    73. 73)
      • 73. Lokan, C.: ‘What should you optimize when building an estimation model?’. Proc. Int. Symp. Software Metrics, Como, Italy, 2005, p. 10.
    74. 74)
      • 74. Braga, P.L., Oliveira, A.L., Meira, S.R.: ‘A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation’. Proc. ACM Symp. on Applied Computing, Fortaleza, Ceara, Brazil, 2008, pp. 17881792.
    75. 75)
      • 75. Huang, S.J., Chiu, N.H., Chen, L.W.: ‘Integration of the grey relational analysis with genetic algorithm for software effort estimation’, Eur. J. Oper. Res., 2008, 188, (3), pp. 898909.
    76. 76)
      • 76. Tsakonas, A., Dounias, G.: ‘Application of genetic programming in software engineering empirical data modelling’. Proc. Int. Conf. ICSOFT, Porto, Portugal, 2008, pp. 295300.
    77. 77)
      • 77. Di Martino, S., Ferrucci, F., Gravino, C., et al: ‘A genetic algorithm to configure support vector machines for predicting fault-prone components’. Proc. Int. Conf. Product Focused Software Process Improvement, Torre Canne, Italy, 2011, pp. 247261.
    78. 78)
      • 78. Rodríguez, D., Ruiz, R., Riquelme, J.C., et al: ‘Searching for rules to detect defective modules: A subgroup discovery approach’, Inf. Sci., 2012, 191, pp. 1430.
    79. 79)
      • 79. Jain, A., Tarwani, S., Chug, A.: ‘An empirical investigation of evolutionary algorithm for software maintainability prediction’. Proc. Int. Conf. Electrical, Electronics and Computer Science, Jinan, China, 2016, pp. 16.
    80. 80)
      • 80. Dolado, J.J.: ‘On the problem of the software cost function’, Inf. Softw. Technol., 2001, 43, (1), pp. 6172.
    81. 81)
      • 81. Singh, Y., Kaur, A., Malhotra, R.: ‘Prediction of software quality model using gene expression programming’. Product-Focused Software Process Improvement, Oulu, Finland, 2009, pp. 4358.
    82. 82)
      • 82. Li, Y.F., Xie, M., Goh, T.N..: ‘A study of mutual information based feature selection for case based reasoning in software cost estimation’, Expert Syst. Appl., 2009, 36, (3), pp. 59215931.
    83. 83)
      • 83. Alaa, F.S., Al-Afeef, A.: ‘A GP effort estimation model utilizing line of code and methodology for NASA software projects’. Proc. Int. Conf. Intelligent Systems Design and Applications, Cairo, Egypt, 2010, pp. 290295.
    84. 84)
      • 84. Araujo, R.D., Oliveira, A.L., Soares, S., et al: ‘An evolutionary morphological approach for software development cost estimation’, Neural Netw., 2012, 32, pp. 285291.
    85. 85)
      • 85. Malhotra, R., Chug, A.: ‘Application of evolutionary algorithms for software maintainability prediction using object-oriented metrics’. Proc. Int. Conf. on Bioinspired Information and Communications Technologies, Boston, Massachusetts, United States, 2014, pp. 348351.
    86. 86)
      • 86. Malhotra, R., Khanna, M.: ‘The ability of search-based algorithms to predict change-prone classes’, Softw. Qual. Prof., 2014, 17, (1), pp. 1731.
    87. 87)
      • 87. Kumar, L., Rath, S.K.: ‘Application of genetic algorithm as feature selection technique in development of effective fault prediction model’. Proc. Int. Conf. Electrical, Computer and Electronics Engineering, Quetta, Pakistan, 2016, pp. 432437.
    88. 88)
      • 88. Li, Y.F., Xie, M., Goh, T.N.: ‘A study of project selection and feature weighting for analogy based software cost estimation’, J. Syst. Softw., 2009, 82, (2), pp. 241252.
    89. 89)
      • 89. Aljahdali, S., Sheta, A.F.: ‘Software effort estimation by tuning COOCMO model parameters using differential evolution’. Proc. Int. Computer Systems and Applications, Hammamet, Tunisia, 2010, pp. 16.
    90. 90)
      • 90. Pendharkar, P.C.: ‘Exhaustive and heuristic search approaches for learning a software defect prediction model’, Eng. Appl. Artif. Intell., 2010, 23, (1), pp. 3440.
    91. 91)
      • 91. Chiu, N.H.: ‘Combining techniques for software quality classification: an integrated decision network approach’, Expert Syst. Appl., 2011, 38, (4), pp. 46184625.
    92. 92)
      • 92. Kumar, L., Naik, D.K., Rath, S.K.: ‘Validating the effectiveness of object-oriented metrics for predicting maintainability’, Procedia Comput. Sci., 2015, 57, pp. 798806.
    93. 93)
      • 93. Liu, Y., Khoshgoftaar, T.: ‘Reducing overfitting in genetic programming models for software quality classification’. Proc. Int. Conf. High Assurance Systems Engineering, Tampa, Florida, 2004, pp. 5665.
    94. 94)
      • 94. Tsakonas, A., Dounias, G.: ‘Predicting defects in software using grammar-guided genetic programming’. Proc. Int. Conf. Artificial Intelligence: Theories, Models and Applications, Syros, Greece, 2008, pp. 413418.
    95. 95)
      • 95. Jin, C., Dong, E.M., Qin, L.N.: ‘Software fault prediction model based on adaptive dynamical and median particle swarm optimization’. Proc. Int. Conf. Multimedia and Information Technology, Hong Kong, 2010, vol. 1, pp. 4447.
    96. 96)
      • 96. Azzeh, M., Nassif, A.B., Banitaan, S.: ‘A better case adaptation method for case-based effort estimation using multi-objective optimization’. Proc. Int. Conf. Machine Learning and Applications, Detroit, MI, USA, 2014, pp. 409414.
    97. 97)
      • 97. Wu, D., Li, J., Bao, C.: ‘Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation’, Soft Comput., 2017, 22, (16), pp. 52995310.
    98. 98)
      • 98. Hochman, R., Khoshgoftaar, T.M., Allen, E.B., et al: ‘Using the genetic algorithm to build optimal neural networks for fault-prone module detection’. Proc. Int. Symp. Software Reliability Engineering, White Plains, NY, USA, 1996, pp. 152162.
    99. 99)
      • 99. Sheta, A.F.: ‘Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects’, J. Comput. Sci., 2006, 2, (2), pp. 118123.
    100. 100)
      • 100. Li, K., Chen, C., Liu, W., et al: ‘Software defect prediction using fuzzy integral fusion based on GA-FM’, Wuhan Univ. J. Nat. Sci., 2014, 19, (5), pp. 405408.
    101. 101)
      • 101. Sheta, A.F., Ayesh, A., Rine, D.: ‘Evaluating software cost estimation models using particle swarm optimisation and fuzzy logic for NASA projects: a comparative study’, Int. J. Bio-Inspired Comput., 2010, 2, (6), pp. 365373.
    102. 102)
      • 102. Can, H., Jianchun, X., Ruide, Z., et al: ‘A new model for software defect prediction using particle swarm optimization and support vector machine’. Proc. Control and Decision Conf. (CCDC), Florence, Italy, 2013, pp. 41064110.
    103. 103)
      • 103. Baqais, A.A., Alshayeb, M., Baig, Z.A.: ‘Hybrid intelligent model for software maintenance prediction’. Proc. World Congress on Engineering, London, UK, 2013, pp. 358362.
    104. 104)
      • 104. Ahmed, F., Bouktif, S., Serhani, A., et al: ‘Integrating function point project information for improving the accuracy of effort estimation’. Proc. Int. Conf. Advanced Engineering Computing and Applications in Sciences, Valencia, Spain, 2008, pp. 193198.
    105. 105)
      • 105. Balogh, G., Végh, Á.Z., Beszédes, Á.: ‘Prediction of software development modification effort enhanced by a genetic algorithm’. Proc. Int. Symp. Search based Software Engineering, Trento, Italy, 2012, pp. 16.
    106. 106)
      • 106. Dan, Z.: ‘Improving the accuracy in software effort estimation: using artificial neural network model based on particle swarm optimization’. Proc. Int. Conf. Service Operations and Logistics, and Informatics, Dongguan, China, 2013, pp. 180185.
    107. 107)
      • 107. Grosan, C., Abraham, A.: ‘Hybrid evolutionary algorithms: methodologies, architectures, and reviews’, Stud. Comput. Intell., 2007, 75, pp. 117.
    108. 108)
      • 108. Soyer, R., Mazzuchi, T.A., Singpurwalla, N.D. (Eds.): ‘Mathematical repository: an expository perspective’ (Springer Science & Business Media, Springer US, 2012).
    109. 109)
      • 109. Shepperd, M., MacDonell, S.: ‘Evaluating prediction systems in software project estimation’, Inf. Softw. Technol., 2012, 54, (8), pp. 820827.
    110. 110)
      • 110. Whigham, P.A., Owen, C.A., Macdonell, S.G.: ‘A baseline model for software effort estimation’, ACM Trans. Softw. Eng. Methodol., 2015, 24, (3), p. 20.
    111. 111)
      • 111. Arcuri, A., Fraser, G.: ‘On parameter tuning in search based software engineering’. Proc. Int. Symp. Search based Software Engineering, Szeged, Hungary, 2011, pp. 3347.
    112. 112)
      • 112. Hall, M.A.: ‘Correlation-based feature selection for discrete and numeric class machine learning’. Proc. Int. Conf. on Machine Learning, Stanford, CA, USA, 2000, pp. 359366.
    113. 113)
      • 113. Ali, S., Briand, L.C., Hemmati, H., et al: ‘A systematic review of the application and empirical investigation of search-based test case generation’, IEEE Trans. Softw. Eng., 2010, 36, (6), pp. 742762.
    114. 114)
      • 114. Sigweni, B., Shepperd, M., Turchi, T.: ‘Realistic assessment of software effort estimation models’. Proc. Int. Conf. Evaluation & Assessment in Software Engineering, Limerick, Ireland, 2016, p. 41.
    115. 115)
      • 115. Foss, T., Stensrud, E., Kitchenham, B., et al: ‘Simulation study of the model evaluation criterion MMRE’, IEEE Trans. Softw. Eng., 2003, 29, (11), pp. 985995.
    116. 116)
      • 116. Langdon, W.B., Dolado, J., Sarro, F., et al: ‘Exact mean absolute error of baseline predictor MARP0’, Inf. Softw. Technol., 2016, 73, pp. 1618.
    117. 117)
      • 117. Sarro, F., Ferrucci, F., Harman, M., et al: ‘Adaptive multi-objective evolutionary algorithms for overtime planning in software projects’, IEEE Trans. Softw. Eng., 2017, 43, (10), pp. 898917.
    118. 118)
      • 118. Ferrucci, F., Harman, M., Ren, J., et al: ‘Not going to take this anymore: multi-objective overtime planning for software engineering projects’. Proc. Int. Conf. on Software Engineering, San Franciso, CA, 2013, pp. 462471.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-sen.2018.5143
Loading

Related content

content/journals/10.1049/iet-sen.2018.5143
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address