Application of Soft Computing Techniques in Global Software Development: state-of-the-art Review

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Developing Software through a globally distributed team is a modern trend, which is not only cost effective but also yields best project results mitigating risk and increasing return on investment. This is easily achieved by ensuring through put in production is maintained at all times irrespective of the clock time and geographical boundaries. This shift of phenomenon is happening across the board as more and more companies use this as a strategic tool. Modern day technology makes this all possible, without compromising quality, coding practices and project management techniques. In this paper we have researched several papers (2008 to 2018) and understood the data for soft computing to provide a strong basis for future directions

     

     


  • Keywords


    Software Development; Global Software Development; Soft Computing; Distributed Team.

  • References


      [1] Shah, Yasir Hassan, Mushtaq Raza, and Sani UlHaq. "Communication issues in GSD." International Journal of Advanced Science and Technology 40.2012 (2012): 69-76 , available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.640.1760&rep=rep1&type=pdf

      [2] Ali, Syed Mubashir, et al. "University Students’ Perception on the Impact of 3G Mobile Broadband in Pakistan-A Survey." Research Inventy: International journal of Engineering and Science 5.2 (2015), available online: http://www.academia.edu/download/36988940/A5200105.pdf

      [3] Al-Zaidi, Areej, and Rizwan Qureshi. "Global software development geographical distance communication challenges." Int. Arab J. Inf. Technol. 14.2 (2017): 215-222, available online: http://www.ccis2k.org/iajit/PDF/Vol%2014,%20No.%202/8063.pdf

      [4] Iftikhar, Asim, et al. "Trust Development in virtual teams to implement global software development (GSD): A structured approach to overcome communication barriers." Engineering Technologies and Social Sciences (ICETSS), 2017 IEEE 3rd International Conference on. IEEE, 2017. https://doi.org/10.1109/ICETSS.2017.8324169

      [5] Anjum, Maria, Muhammad Islam Zafar, and Syed Atif Mehdi. "Establishing guidelines for management of virtual teams." IADIS Virtual Multi Conference on Computer Science and Information Systems (Software Engineering and Applications). 2006.

      [6] Casey, Valentine. "Imparting the importance of culture to global software development." ACM inroads 1.3 (2010): 51-57, available online: https://dl.acm.org/citation.cfm?id=1835443

      [7] Wu, Shujian. "Overview of communication in global software development process." Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference on. IEEE, 2012. https://doi.org/10.1109/SOLI.2012.6273583

      [8] Casey, Valentine, and Ita Richardson. "Implementation of Global Software Development: a structured approach." Software Process: Improvement and Practice 14.5 (2009): 247-262, available online: https://onlinelibrary.wiley.com/doi/full/10.1002/spip.422

      [9] Ghaffari, Mona, Farrokh Sheikhahmadi, and Gholamreza Safakish. "Modeling and risk analysis of virtual project team through project life cycle with fuzzy approach." Computers & Industrial Engineering 72 (2014): 98-105, available online: https://www.sciencedirect.com/science/article/abs/pii/S0360835214000540

      [10] S. A. Burney, S. M. Ali, and S. Burney, “A survey of soft computing applications for decision making in supply chain management,” in Engineering Technologies and Social Sciences (ICETSS), 2017 IEEE 3rd International Conference on, 2017, pp. 1–6. https://doi.org/10.1109/ICETSS.2017.8324158

      [11] A. Iftikhar, S. Musa, M. Alam, M. M. Su'ud and S. M. Ali, "A survey of soft computing applications in global software development," 2018 IEEE International Conference on Innovative Research and Development (ICIRD), Bangkok, Thailand, 2018, pp. 1-4. https://doi.org/10.1109/ICIRD.2018.8376330

      [12] Yu, Liguo, and Alok Mishra. "Risk analysis of global software development and proposed solutions." Automatika 51.1 (2010): 89-98, available online: http://www.tandfonline.com/doi/pdf/10.1080/00051144.2010.11828358

      [13] Prikladnicki, Rafael, and Marcelo Hideki Yamaguti. "Risk management in global software development: A position paper." Third International Workshop on Global Software Development. 2004. http://dx.doi.org/10.1049/ic:20040306

      [14] Kaur, Arvinder, Kamaldeep Kaur, and Ruchika Malhotra. "Soft computing approaches for prediction of software maintenance effort." International Journal of Computer Applications 1.16 (2010), available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.206.3368&rep=rep1&type=pdf

      [15] Baisch, Ekkehard, and Thomas Liedtke. "Comparison of conventional approaches and soft-computing approaches for software quality prediction." Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation. 1997 IEEE International Conference on. Vol. 2. IEEE, 1997. https://doi.org/10.1109/ICSMC.1997.638086

      [16] Kiran, N. Raj, and Vadlamani Ravi. "Software reliability prediction by soft computing techniques." Journal of Systems and Software 81.4 (2008): 576-583, available online: https://www.sciencedirect.com/science/article/pii/S0164121207001227

      [17] Hu, Yong, et al. "Software project risk analysis using Bayesian networks with causality constraints." Decision Support Systems 56 (2013): 439-449, available online: https://www.sciencedirect.com/science/article/pii/S0167923612003338

      [18] Bhatia, Nitin, and Namarta Kapoor. "Fuzzy cognitive map based approach for software quality risk analysis." ACM SIGSOFT Software Engineering Notes 36.6 (2011): 1-9, available online: https://dl.acm.org/citation.cfm?id=2047422

      [19] A. Nieto-Morote and F. Ruz-Vila, “A fuzzy approach to construction project risk assessment,” International Journal of Project Management, vol. 29, no. 2, pp. 220–231, 2011, available online: https://www.sciencedirect.com/science/article/pii/S0263786310000268

      [20] S. Hartmann, “A competitive genetic algorithm for resource-constrained project scheduling,” Naval Research Logistics (NRL), vol. 45, no. 7, pp. 733–750, 1998, available online: https://www.sciencedirect.com/science/article/pii/S0263786310000268

      [21] Sheta, Alaa, David Rine, and Aladdin Ayesh. "Development of software effort and schedule estimation models using soft computing techniques." Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on. IEEE, 2008. https://doi.org/10.1109/CEC.2008.4630961

      [22] Chadli, Saad Yasser, et al. "Identifying risks of software project management in Global Software Development: An integrative framework." Computer Systems and Applications (AICCSA), 2016 IEEE/ACS 13th International Conference of. IEEE, 2016. https://doi.org/10.1109/AICCSA.2016.7945664

      [23] Pilatti, Leonardo, and Jorge Luis Nicolas Audy. "Global software development offshore insourcing organizations characteristics: Lessons learned from a case study." Global Software Engineering, 2006. ICGSE'06. International Conference on. IEEE, 2006. https://doi.org/10.1109/ICGSE.2006.261244

      [24] Prikladnicki, Rafael, Jorge Luis Nicolas Audy, and Roberto Evaristo. "A reference model for global software development: findings from a case study." Global Software Engineering, 2006. ICGSE'06. International Conference on. IEEE, 2006. https://doi.org/10.1109/ICGSE.2006.261212

      [25] g Yan, Zhen. "Efficient Maintenance Support in Offshore Software Development: a Case Study on a Global E-Commerce Project." The 3rd International Workshop on Global Software Development (2004). http://gsd2004.cs.uvic.ca/docs/proceedings.pdf#page=15

      [26] Aljahdali, Sultan H., and Mohammed E. El-Telbany. "Software reliability prediction using multi-objective genetic algorithm." Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on. IEEE, 2009. https://doi.org/10.1109/AICCSA.2009.5069339

      [27] Mittal, Harish, and Pradeep Bhatia. "Software maintainability assessment based on fuzzy logic technique." ACM SIGSOFT Software Engineering Notes 34.3 (2009): 1-5, available online: https://dl.acm.org/citation.cfm?id=1527210

      [28] Erturk, Ezgi, and Ebru Akcapinar Sezer. "A comparison of some soft computing methods for software fault prediction." Expert Systems with Applications 42.4 (2015): 1872-1879, available online: https://www.sciencedirect.com/science/article/pii/S0957417414006496

      [29] Idri, Ali, Alain Abran, and T. M. Khoshgoftaar. "Fuzzy analogy: A new approach for software cost estimation." International Workshop on Software Measurement. 2001.

      [30] K. Khatatneh and T. Mustafa, “Software reliability modeling using soft computing technique,” European Journal of Scientific Research, vol. 26, no. 1, pp. 154–160, 2009, available online: https://www.researchgate.net/profile/Khatatneh_Khalaf/publication/241821620_Software_Reliability_Modeling_Using_Soft_Computing_Technique/links/58a769b1aca27206d9ac3e71/Software-Reliability-Modeling-Using-Soft-Computing-Technique.pdf

      [31] Yadav, Harikesh Bahadur, and Dilip Kumar Yadav. "A fuzzy logic based approach for phase-wise software defects prediction using software metrics." Information and Software Technology 63 (2015): 44-57, available online: https://www.sciencedirect.com/science/article/pii/S095058491500052X

      [32] H. Yang, “Measuring software product quality with ISO standards base on fuzzy logic technique,” in Affective Computing and Intelligent Interaction, Springer, 2012, pp. 59–67, available online:https://link.springer.com/chapter/10.1007/978-3-642-27866-2_8

      [33] Challa, Jagat Sesh, et al. "Integrated Software Quality Evaluation: A Fuzzy Multi-Criteria Approach." JIPS 7.3 (2011): 473-518. available Online, http://arindampaul.yolasite.com/resources/JIPS_v07_no3_paper07.pdf

      [34] Singh, Yogesh, Pradeep Kumar Bhatia, and Omprakash Sangwan. "Predicting software maintenance using fuzzy model." ACM SIGSOFT Software Engineering Notes 34.4 (2009): 1-6, available online https://dl.acm.org/citation.cfm?id=1543425

      [35] Mittal, Harish, and Pradeep Bhatia. "Software maintainability assessment based on fuzzy logic technique." ACM SIGSOFT Software Engineering Notes 34.3 (2009): 1-5. https://dl.acm.org/citation.cfm?id=1527210

      [36] Lu, Kun-Yung, and Chun-Chin Sy. "A real-time decision-making of maintenance using fuzzy agent." Expert Systems with Applications 36.2 (2009): 2691-2698, available online: https://www.sciencedirect.com/science/article/pii/S0957417408000365

      [37] Mittal, J. P., Pradeep Bhatia, and Harish Mittal. "Software maintenance productivity assessment using fuzzy logic." ACM SIGSOFT Software Engineering Notes 34.5 (2009): 1-4. https://dl.acm.org/citation.cfm?id=1598739

      [38] Pandey, Ajeet Kumar, and N. K. Goyal. "A fuzzy model for early software fault prediction using process maturity and software metrics." International Journal of Electronics Engineering 1.2 (2009): 239-245, available online: https://www.researchgate.net/profile/Neeraj_Goyal3/publication/230739115_A_Fuzzy_Model_for_Early_Software_Fault_Prediction_Using_Process_Maturity_and_Software_Metrics/links/0fcfd503c6b13f1347000000/A-Fuzzy-Model-for-Early-Software-Fault-Prediction-Using-Process-Maturity-and-Software-Metrics.pdf

      [39] Pandey, Ajeet Kumar, and Neeraj Kumar Goyal. "Predicting fault-prone software module using data mining technique and fuzzy logic." International Journal of Computer and Communication Technology 2.2 (2010): 56-63, available online: https://www.researchgate.net/profile/Neeraj_Goyal3/publication/228744278_Predicting_Fault-prone_Software_Module_Using_Data_Mining_Technique_and_Fuzzy_Logic/links/0fcfd50921eaa65718000000/Predicting-Fault-prone-Software-Module-Using-Data-Mining-Technique-and-Fuzzy-Logic.pdf

      [40] Mittal, Anish, Kamal Parkash, and Harish Mittal. "Software cost estimation using fuzzy logic." ACM SIGSOFT Software Engineering Notes 35.1 (2010): 1-7. https://dl.acm.org/citation.cfm?id=1668866

      [41] Attarzadeh, Iman, and Siew Hock Ow. "Improving the accuracy of software cost estimation model based on a new fuzzy logic model." World applied sciences Journal 8.2 (2010): 177-184, available online: https://www.researchgate.net/profile/Jack_Son6/post/Need_latest_articles_of_Software_Cost_Estimation_of_FPA_using_Fuzzy_Logic/attachment/5a3bf1ecb53d2f0bba4605d4/AS:574060074422272@1513877995874/download/Improving_the_Accuracy_of_Software_Cost.pdf

      [42] Andreou, Andreas S., and Efi Papatheocharous. "Software cost estimation using fuzzy decision trees." Automated Software Engineering, 2008. ASE 2008. 23rd IEEE/ACM International Conference on. IEEE, 2008. http://ieeexplore.ieee.org/abstract/document/4639344/

      [43] Kazemifard, Mohammad, et al. "Fuzzy emotional COCOMO II software cost estimation (FECSCE) using multi-agent systems." Applied Soft Computing 11.2 (2011): 2260-2270, available online: https://www.sciencedirect.com/science/article/pii/S1568494610002085

      [44] Du, Wei Lin, Danny Ho, and Luiz Fernando Capretz. "Improving software effort estimation using neuro-fuzzy model with SEER-SEM." arXiv preprint arXiv:1507.06917 (2015), available online: https://arxiv.org/abs/1507.06917

      [45] PVGD, Prasad Reddy. "Particle swarm optimization in the fine-tuning of fuzzy software cost estimation models." International Journal of Software Engineering (IJSE) 1.2 (2010): 12-23, available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.7693&rep=rep1&type=pdf

      [46] Chang Lee, Kun, Namho Lee, and Honglei Li. "A particle swarm optimization‐driven cognitive map approach to analyzing information systems project risk." Journal of the Association for Information Science and Technology 60.6 (2009): 1208-1221, available online: http://onlinelibrary.wiley.com/doi/10.1002/asi.21019/full

      [47] Zheng, Jun. "Predicting software reliability with neural network ensembles." Expert systems with applications 36.2 (2009): 2116-2122, available online: https://www.sciencedirect.com/science/article/pii/S0957417407006628

      [48] Ma, Changjie, Guochang Gu, and Jing Zhao. "A Novel Software Reliability Assessment Approach based on Neural Network in Network Environment." IJACT: International Journal of Advancements in Computing Technology 4.1 (2012): 136-144, available online: https://pdfs.semanticscholar.org/04a1/7c4f6c0a0d192c4c9c1118c8221f0639323e.pdf

      [49] Zheng, Jun. "Cost-sensitive boosting neural networks for software defect prediction." Expert Systems with Applications37.6 (2010): 4537-4543, available online: https://www.sciencedirect.com/science/article/pii/S0957417409011026

      [50] Shukla, Ruchi, and Arun Kumar Misra. "Estimating software maintenance effort: a neural network approach." Proceedings of the 1st India software engineering conference. ACM, 2008. https://dl.acm.org/citation.cfm?id=1342232

      [51] Dash, Yajnaseni, Sanjay Kumar Dubey, and Ajay Rana. "Maintainability prediction of object oriented software system by using artificial neural network approach." International Journal of Soft Computing and Engineering (IJSCE) 2.2 (2012): 420-423, available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.452.7737&rep=rep1&type=pdf

      [52] Al-Jamimi, Hamdi A., and Lahouari Ghouti. "Efficient prediction of software fault proneness modules using support vector machines and probabilistic neural networks." Software Engineering (MySEC), 2011 5th Malaysian Conference in. IEEE, 2011. http://ieeexplore.ieee.org/abstract/document/6140679/

      [53] Jin, Cong, and Shu-Wei Jin. "Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization." Applied Soft Computing35 (2015): 717-725, available online: https://www.sciencedirect.com/science/article/pii/S1568494615004366

      [54] Kumar, K. Vinay, et al. "Software development cost estimation using wavelet neural networks." Journal of Systems and Software 81.11 (2008): 1853-1867, available online: https://www.sciencedirect.com/science/article/pii/S0164121208000071

      [55] Gharehchopogh, Farhad Soleimanian. "Neural networks application in software cost estimation: a case study." Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on. IEEE, 2011. http://ieeexplore.ieee.org/abstract/document/5946160/

      [56] Rao, B. Tirimula, et al. "A novel neural network approach for software cost estimation using Functional Link Artificial Neural Network (FLANN)." International Journal of Computer Science and Network Security 9.6 (2009): 126-131, available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.494.1694&rep=rep1&type=pdf

      [57] Attarzadeh, Iman, and Siew Hock Ow. "Proposing a new software cost estimation model based on artificial neural networks." Computer Engineering and Technology (ICCET), 2010 2nd International Conference on. Vol. 3. IEEE, 2010. http://ieeexplore.ieee.org/abstract/document/5485840/

      [58] Yong, Hu, et al. "A neural networks approach for software risk analysis." Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. IEEE, 2006. http://ieeexplore.ieee.org/abstract/document/4063720/

      [59] Fenton, N., Martin Neil, and D. Marquez. "Using Bayesian networks to predict software defects and reliability." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 222.4 (2008): 701-712. http://journals.sagepub.com/doi/abs/10.1243/1748006XJRR161

      [60] Doguc, Ozge, and Jose Emmanuel Ramirez-Marquez. "A generic method for estimating system reliability using Bayesian networks." Reliability Engineering & System Safety94.2 (2009): 542-550, available online: https://www.sciencedirect.com/science/article/pii/S0951832008001804

      [61] Okutan, Ahmet, and Olcay Taner Yıldız. "Software defect prediction using Bayesian networks." Empirical Software Engineering 19.1 (2014): 154-181, available online: https://link.springer.com/article/10.1007/s10664-012-9218-8

      [62] Khomh, Foutse, et al. "A bayesian approach for the detection of code and design smells." Quality Software, 2009. QSIC'09. 9th International Conference on. IEEE, 2009. http://ieeexplore.ieee.org/abstract/document/5381430/

      [63] de Melo, Ana CV, and Adilson J. Sanchez. "Software maintenance project delays prediction using Bayesian Networks." Expert Systems with Applications 34.2 (2008): 908-91, available online: https://www.sciencedirect.com/science/article/pii/S0957417406003526

      [64] Weber, Philippe, et al. "Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas." Engineering Applications of Artificial Intelligence 25.4 (2012): 671-682, available online: https://www.sciencedirect.com/science/article/pii/S095219761000117X

      [65] Catal, Cagatay, and Banu Diri. "Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem." Information Sciences 179.8 (2009): 1040-1058, available online: https://www.sciencedirect.com/science/article/pii/S0020025508005173

      [66] Dejaeger, Karel, Thomas Verbraken, and Bart Baesens. "Toward comprehensible software fault prediction models using bayesian network classifiers." IEEE Transactions on Software Engineering 39.2 (2013): 237-257. http://ieeexplore.ieee.org/abstract/document/6175912/

      [67] Khodakarami, Vahid, and Abdollah Abdi. "Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items." International Journal of Project Management 32.7 (2014): 1233-1245, available online: https://www.sciencedirect.com/science/article/pii/S0263786314000027

      [68] Lins, Isis Didier, et al. "A particle swarm‐optimized support vector machine for reliability prediction." Quality and Reliability Engineering International 28.2 (2012): 141-158. http://onlinelibrary.wiley.com/doi/10.1002/qre.1221/full

      [69] Can, He, et al. "A new model for software defect prediction using particle swarm optimization and support vector machine." Control and Decision Conference (CCDC), 2013 25th Chinese. IEEE, 2013. http://ieeexplore.ieee.org/abstract/document/6561670/

      [70] Wahono, Romi Satria, and Nanna Suryana. "Combining particle swarm optimization based feature selection and bagging technique for software defect prediction." International Journal of Software Engineering and Its Applications 7.5 (2013): 153-166, available online: http://romisatriawahono.net/lecture/rm/paper/Wahono%20-%20pso%20bagging%20for%20sdp%20-%20%202013.pdf

      [71] Cao, Ping, and FuJi Chen. "A risk control optimization model for software project." Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on. IEEE, 2009. http://ieeexplore.ieee.org/abstract/document/5362886/

      [72] Azar, Danielle, and Joseph Vybihal. "An ant colony optimization algorithm to improve software quality prediction models: Case of class stability." Information and Software Technology 53.4 (2011): 388-393, available online: https://www.sciencedirect.com/science/article/pii/S0950584910002144

      [73] Xiao, Jing, Xian-Ting Ao, and Yong Tang. "Solving software project scheduling problems with ant colony optimization." Computers & Operations Research 40.1 (2013): 33-46, available online: https://www.sciencedirect.com/science/article/pii/S0305054812001104

      [74] Sun, Peng, and Xiaoping Wang. "Application of ant colony optimization in preventive software maintenance policy." Information Science and Technology (ICIST), 2012 International Conference on. IEEE, 2012. http://ieeexplore.ieee.org/abstract/document/6221624/

      [75] Maleki, Isa, Ali Ghaffari, and Mohammad Masdari. "A new approach for software cost estimation with hybrid genetic algorithm and ant colony optimization." International Journal of Innovation and Applied Studies 5.1 (2014): 72, available online: http://search.proquest.com/openview/7b475b6eca4598c05982badd77802a59/1?pq-origsite=gscholar&cbl=2031961

      [76] Martens, Anne, et al. "Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms." Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering. ACM, 2010. https://dl.acm.org/citation.cfm?id=1712624

      [77] Tavakkoli-Moghaddam, Reza, Jalal Safari, and Farrokh Sassani. "Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm." Reliability Engineering & System Safety 93.4 (2008): 550-556, available online: https://www.sciencedirect.com/science/article/pii/S0951832007000579

      [78] Aljahdali, Sultan H., and Mohammed E. El-Telbany. "Genetic algorithms for optimizing ensemble of models in software reliability prediction." International Journal on Artificial Intelligence and Machine Learning (AIML) ICGST 8.1 (2008): 5-13, available online: http://www.academia.edu/download/39928454/AIML-Volume8-issue1-P1121546431.pdf#page=9

      [79] Di Martino, Sergio, et al. "A genetic algorithm to configure support vector machines for predicting fault-prone components." International Conference on Product Focused Software Process Improvement. Springer, Berlin, Heidelberg, 2011, available online: https://link.springer.com/10.1007%2F978-3-642-21843-9_20

      [80] Reyes, Francisco, et al. "The optimization of success probability for software projects using genetic algorithms." Journal of Systems and Software 84.5 (2011): 775-785, available online: https://www.sciencedirect.com/science/article/pii/S0164121210003456

      [81] Maleki, Isa, Laya Ebrahimi, and Farhad Soleimanian Gharehchopogh. "A hybrid approach of firefly and genetic algorithms in software cost estimation." Magnt Research Report 2.6 (2014): 372-388, available online: http://brisjast.com/wp-content/uploads/2015/06/Nov-50-2014.pdf

      [82] Ghatasheh, Nazeeh, et al. "Optimizing software effort estimation models using firefly algorithm." Journal of Software Engineering and Applications 8.03 (2015): 133, available online: https://www.researchgate.net/profile/Nazeeh_Ghatasheh/publication/273136303_Optimizing_Software_Effort_Estimation_Models_Using_Firefly_Algorithm/links/55095cd30cf26ff55f855453.pdf

      [83] Arora, Ishani, and Anju Saha. "Software fault prediction using firefly algorithm." International Journal of Intelligent Engineering Informatics 6.3-4 (2018): 356-377, available online https://www.inderscienceonline.com/doi/abs/10.1504/IJIEI.2018.091870

      [84] Magro, Micaela Caserza, and Paolo Pinceti. "A confirmation technique for predictive maintenance using the Rough Set Theory." Computers & Industrial Engineering 56.4 (2009): 1319-1327, available online https://www.sciencedirect.com/science/article/pii/S0360835208001678


 

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Article ID: 23015
 
DOI: 10.14419/ijet.v7i4.15.23015




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