Open Access

ARTICLE

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Muhammad Hasnain¹, Imran Ghani², Seung Ryul Jeong^3,*, Muhammad Fermi Pasha⁴, Sardar Usman⁵, Anjum Abbas⁶

1 Lahore Leads University, Lahore, 42000, Pakistan
2 Computer and Information Sciences, Virginia Military Institute, Lexington, VA, 24450, USA
3 Graduate School of Business IT, Kookmin University, Seoul, 136, Korea
4 School of Information Technology, Monash University, Bandar Sunway, 47500, Malaysia
5 Department of Computer Science and Software Engineering, Grand Asian University Sialkot, 51040, Pakistan
6 Government Sarwar Shaheed Associate College, Gujjar Khan, Rawalpindi, 46000, Pakistan

* Corresponding Author: Seung Ryul Jeong. Email:

Computers, Materials & Continua 2023, 74(1), 783-799. https://doi.org/10.32604/cmc.2023.030162

Received 19 March 2022; Accepted 13 June 2022; Issue published 22 September 2022

Abstract

Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors, misinterpretation, and miscommunication of requirements and decision-making processes play their roles in software success forecasting. We discussed a potential relationship between forecasting of software success or failure and the development processes. We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study, and they were more likely to rate software success forecasting linking to the development processes. Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate. The results of this empirical study also validated that requirements’ misinterpretation and miscommunication were the main causes behind software systems’ failure. It has been seen that reliable, relevant, and trustworthy sources of information help in decision-making to predict software systems’ success in the software industry. This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects. Future investigation can be performed to identify the other human factors that may impact software systems’ success.

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Keywords

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