Software risk prediction by Grasshopper Optimization Algorithm and Machine learning

Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems

bahar ahmadi ¹ , Hadi Khosravi-farsani ² , Taghi Javdani Gandomani ³

1 - MSc. Student, Department of Computer Engineering, Shahrekord University, Shahrekord, Iran
2 - Assistant Professor, Department of Computer Engineering, Shahrekord University, Shahrekord, Iran
3 - Assistant Professor, Department of Computer Engineering, Shahrekord University, Shahrekord, Iran

Received: 2022-06-09 Accepted : 2022-11-17 Published : 2022-09-23

Keywords: Classification methods, Locust optimization, Simulated Annealing Algorithm, risk management, Possible risks,

Abstract :

Software development can be considered an activity that uses various technological advances and requires high knowledge. For this reason, every software development project contains elements of uncertainty known as project risk. The success of a software development project is highly dependent on the amount of risk associated with each project activity. Therefore, as a project manager, awareness of risks is not enough. In order to achieve a successful outcome, a project manager must be able to identify, then evaluate, prioritize, and ultimately manage all major risks.Risk management is a process to identify, eliminate and predict possible risks. In other words, this process is related to all the activities that are done to reduce the uncertainty associated with specific tasks or events. Risk management focuses on identifying risks and dealing with them appropriately. Projects have individual or general risks. Some of these risks are related to a specific activity and others to the project. Usually, risks are first identified and associated with project activities. Determining how people behave to achieve strategic activity goals is to identify risks. One of the methods of improvement is to take help from new algorithms and use machine learning techniques in the process of identifying or predicting possible risks.The use of different algorithms and techniques to identify software risks has always attracted the attention of experts. In this study, the grasshopper optimization algorithm has been used to improve classification accuracy and increase accuracy as well as reduce specificity. In this method, by combining the proposed algorithm with the support vector machine algorithm, it is used to obtain better and more acceptable results in data with large dimensions in order to reduce the dimension and also select the feature, which has attracted the attention of many researchers.The grasshopper optimization algorithm has not been used in the risk prediction system of software projects, the obtained results have shown that this proves the applicability and strength of the grasshopper optimization algorithm for solving problems with unknown and real search spaces.The purpose of this study is to predict the risks of software projects with the help of the grasshopper optimization algorithm. In this method, feature selection and reduction is done by the grasshopper optimization algorithm, and vector machine classification methods are used to classify risk and features.

References:

 

 

[1]  

GouthamanP, “Prediction of Risk Percentage in Software

     Projects by Training Machine Learning Classifiers Computers

     & Electrical Engineering”, Volume 94, September 2021.

[3]     RIZWAN M et al, “Analyses of Classifier’s Performance
     Measures Used in Software Fault Prediction Studies”. IEEE
     Access 2019, 7:82764–75.

[4]        MattosDFM et al, “An aglie effective network functionvir
     tualization in infrastucture for the Internet of Things”.J
     Internet, volume 10, Article number:6 (2019).

[5]     Dam HK et al, “Towards Effective AI-Powered Agile
     Project Management”, 2019 IEEE/ACM 41st International
     Conference on Software Engineering: New Ideas and
     Emerging Result (ICSE-NIER). Pages 41–44

[6]    SH.Saremi, Mirjalali, A.Lewi (2017), “GrasshopperOptimis
    ationAlgorithm”: Theory and application, Advances in Engi
     neering Software, Volume 105, Pages 30-47,2017.

[7]    N.Paltrinieri et al,”Learning about risk:Machine learning for
      risk assessment”,Safety Science,Vol 118,Pages‌ 475-
      486,October 2019.

[8]   L.Bai et al,”Project Portfolio Resource Risk Assessment‌
     considering Project Interdependency by the Fuzzy Bayesian
     Network”, Complexity, vol. 2020, Article ID 5410978,21
     pages, 2020.

[9]      L.Radu,”Effort Prediction in Agile Software Development
     with Bayesian,Networks”, Proceedings of the International
     Conference on Software Technologies (ICSOFT 2019).
     Pages 238–245.

[10]  L. Pan et al, “An Improved CNN Model for Within-
      Project Software Delect Prediction”, Journals Applied Vol 9,
      Applied Sciences 9(10):2138,2019.

[11]   Sai Mohan et al.”A Survey on Software Cost Estimation
       Techniques”,School of Electrical Engineering & Computer
       Science,University of North Dakota,Grand Forks,USA
        Vol.12 No.6, Pages 23 June 2019.7

[12]  A. Puri et al,”Risk Management in Software Engineering
       Using Big Data”,International Conference on Intelligent
      Engineering and Management, 17-19 June 2020.

[13]  M. Trzeciak et al,”Sustainable Risk Management in IT
      Enterprises,Advances in Sutainable Rick Management”, Jour
      nals Risks Vol 9,2021.

[14]     Aru,O, et al: “OPTIMIZATION OF SOFTWARE RISK
       ASSESSMENT MODEL USING GENETICAL
       ALGORITHM”,117 Umudike Journal of Engineering and
    Technology(UJET);Vol.5, No 1,June 2019, pp.117
    123;Michael Okpara University of Agriculture,
     Umudike,Print‌ISSN:2536-7404,Electronic   ISSN: 2545-525
     7;https://ujetmouau. net; DOI:https://doi.org/10.33922/j.ujet_
     v5i1 13.

[15]  M. Alanis et al,”Particle Swarm Optimization for
      Predicting the Development Effort of Software Projects”
      ,Mathematics2020,8,1819;doi:10.3390/math81018
        19.

[16]   Ms. Manju Vyas et al,”Effect of Dimensionality
      Reduction on Prediction Accuracy of Effort of Agile
      Projects Using Principal Component Analysis”, IOP
      Conf. Series: Materials Science and Engineering,2021.

[17]  Juan Murillo-Morera, Christian Quesada-López,”A
       genetic algorithm based framework for software efforT
       prediction”, Journal of Software Engineering Research
       and Development 5, Article number: 4 (2017)

[18]     Mariana Dayanara Alanis, “Particle Swarm Optimization
       for Predicting the Development Effort of Software Projects”,
        Mathematics 2020, 8(10).

[19]     Pospieszny, P.Czarnacka-Chrobot, B.Kobylinski,A. “An
       effective approach for software project effort and duration
       estimation with machine learning algorithms”. J. Syst.
       Softw.2018, 137, 184–196

[20]  Li, Z. Jing, X.Y.” Zhu, X.Progress on approaches to
       software defect prediction”.IET Softw. 2018, 12, 161–175.