A comparative analysis of soft computing techniques in software fault prediction model development

Deepak Sharma1, P. Chandra1
1University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka, India

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Erturk E, Sezer EA (2015) A comparison of some soft computing methods for software fault prediction. Expert Syst Appl 42(4):1872–1879. Available: http://www.sciencedirect.com/science/article/pii/S0957417414006496

Mockus A, Weiss DM (2002) Predicting risk of software changes. Bell Labs Tech J 5(2):169–180. http://ieeexplore.ieee.org/abstract/document/6772130/

Zadeh LA (1994) Soft computing and fuzzy logic. IEEE Softw 11(6):48–56. http://ieeexplore.ieee.org/abstract/document/329401/

Chaturvedi DK (2008) Soft computing: techniques and its applications in electrical engineering, vol 103. Springer, New York. http://www.springer.com/in/book/9783540774808

Mohanty R, Ravi V, Patra MR (2010) The application of intelligent and soft-computing techniques to software engineering problems: a review. Int J Inf Decision Sciences 2(3):233–272. https://doi.org/10.1504/IJIDS.2010.03345

Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5–8):445–464. https://doi.org/10.1007/s00170-009-2104-x

Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

Honkela T, Kaski S, Lagus K, Kohonen T (1997) WEBSOM—self-organizing maps of document collections. Proc WSOM 97:4–6. https://pdfs.semanticscholar.org/65f7/98edabe8e385647abd384ee8b92ce11a69d1.pdf

Gersho A, Gray RM (2012) Vector quantization and signal compression, vol. 159, Springer Science & Business Media, New York. http://www.springer.com/in/book/9780792391814

Madsen H, Thyregod P, Burtschy B, Albeanu G, Popentiu F (2006) On using soft computing techniques in software reliability engineering. Int J Reliab Qual Saf Eng 13(01):61–72. https://doi.org/10.1142/S0218539306002094

Eiben AE, Schoenauer M (2002) Evolutionary computing. Inf Process Lett 82(1):1–6

Kung SY (2014) Kernel methods and machine learning. Cambridge University Press, Cambridge. https://www.cambridge.org/core/books/kernel-methods-and-machine-learning/4B52092A98E1553A26EB5271D832D29E

Garnier S, Gautrais J, Theraulaz G (2007) The biological principles of swarm intelligence. Swarm Intell 1(1):3–31. https://doi.org/10.1007%2Fs11721-007-0004-y?LI=true

Ardelt M (2004) Wisdom as expert knowledge system: a critical review of a contemporary operationalization of an ancient concept. Hum Dev 47(5):257–285. https://www.karger.com/Article/Abstract/79154

Boccaletti S, Grebogi C, Lai YC, Mancini H, Maza D (2000) The control of chaos: theory and applications. Phys Rep 329(3):103–197

Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38(2):325–339

Neapolitan RE (2012) Probabilistic reasoning in expert systems: theory and algorithms. CreateSpace Independent Publishing Platform, California. https://dl.acm.org/citation.cfm?id=2385835

Ardil E (2010) A soft computing approach for modeling of severity of faults in software systems. Int J Phys Sci 5(2):74–85. http://www.academicjournals.org/journal/IJPS/article-abstract/587073122652

Seliya N, Khoshgoftaar TM, Hulse JV (2010) Predicting faults in high assurance software. In: High-assurance systems engineering (HASE), 2010 IEEE 12th international symposium on, IEEE, pp. 26–34. http://ieeexplore.ieee.org/abstract/document/5634306/

Twala B (2011) Predicting software faults in large space systems using machine learning techniques. Def Sci J 61(4):306–316. http://publications.drdo.gov.in/ojs/index.php/dsj/article/view/1088

Chiu NH (2011) Combining techniques for software quality classification: An integrated decision network approach. Expert Syst Appl 38(4):618–4625

Pelayo L, Dick S (2012) Evaluating stratification alternatives to improve software defect prediction. IEEE Trans Reliab 61(2):516–525. http://ieeexplore.ieee.org/abstract/document/6156808/

Kam J, Dick S (2006) Comparing nearest-neighbour search strategies in the SMOTE algorithm. Can J Electr Comput Eng 31(4):203–210. http://ieeexplore.ieee.org/abstract/document/4028919/

Ramani RG, Kumar SV, Jacob SG (2012) Predicting fault-prone software modules using feature selection and classification through data mining algorithms. In: Computational intelligence & computing research (ICCIC), 2012 IEEE international conference on, IEEE, pp. 1–4. http://ieeexplore.ieee.org/abstract/document/6510294/

Dejaeger K, Verbraken T, Baesens B (2013) Toward comprehensible software fault prediction models using bayesian network classifiers. IEEE Trans Softw Eng 39(2):237–257. http://ieeexplore.ieee.org/abstract/document/6175912/

Chatterjee S, Roy A (2015) Novel algorithms for web software fault prediction. Qual Reliab Eng Int 31(8):1517–1535. https://doi.org/10.1002/qre.1687/full

Goyal R, Chandra P, Singh Y (2014) Suitability of KNN regression in the development of interaction based software fault prediction models. IERI Proc 6:15–21

Arar ÖF, Ayan K (2015) Software defect prediction using cost-sensitive neural network. Appl Soft Comput 33:263–277

Erturk E, Sezer EA (2016) Iterative software fault prediction with a hybrid approach. Appl Soft Comput 49:1020–1033

Chatterjee S, Nigam S, Roy A (2016) Software fault prediction using neuro-fuzzy network and evolutionary learning approach. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2437-y

Dhanajayan RCG, Pillai SA (2017) SLMBC: spiral life cycle model-based Bayesian classification technique for efficient software fault prediction and classification. Soft Comput 21(2):403–415. https://doi.org/10.1007/s00500-016-2316-6