Document Type : Original Article

Authors

1 Department of Statistics, Mathematics and Computer Science, Allameh Tabataba'i University, Tehran, Iran.

2 Department of Instructional Technology, Allameh Tabataba'i University, Tehran, Iran.

Abstract

In today's world, software tools play an important role in speeding up software development, reducing development costs and human efforts, as well as increasing reliability. In software development by tools, choosing a suitable tool will be a difficult task because many of them are available with different capabilities. On the other hand, little research has focused on the classification of these tools and their comparison. This paper aims to perform a literature review of software development tools and to propose architectures for the requirement of the Organization of Small Industries and Industrial Towns of Iran (OSIITI), in Iran. We did a survey over more than 50 software development and programming tools. The results of this survey identified ten classes, namely (a) Database Tools; (b) Integrated Development Environment; (c) Software Development Frameworks; (d) Data Science Tools; (e) Source Control Tools; (f) DevOps Tools; (g) Unified modeling Language (UML) Tools; (h) Cloud Tools for Software Development; (h) Prototyping Tools; and (j) Notifications Programs. For each class, we collected the most software tools that are currently used with their major features. After that, two architectures, based on layered and service-oriented patterns are proposed for OSIITI. The ten specified classes, along with the tools in each class, are very useful for organizations like OSIITI who want to develop software, for both small and large projects.

Keywords

Chen, S. (2023). A counterinsurgent (COIN) framework to defend against consumer activists. Journal of Brand Management, 30(4), 275-301.
Larsson, C. (2022). Cognitive Load in Advanced Systems: Registration of Swedish grades in the University Admissions process.
De Carlo, G., Langer, P., & Bork, D. (2022, October). Advanced visualization and interaction in GLSP-based web modeling: realizing semantic zoom and off-screen elements. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems (pp. 221-231).
Chakraborty, S., & Aithal, P. S. (2022). A Practical Approach to GIT Using Bitbucket, GitHub and SourceTree. International Journal of Applied Engineering and Management Letters (IJAEML), 6(2), 254-263.
Sommerville, I. (2020). Engineering software products (Vol. 355). London, UK: Pearson.
Barlas, P., Lanning, I., & Heavey, C. (2015). A survey of open-source data science tools. International Journal of Intelligent Computing and Cybernetics, 8(3), 232-261.
Portillo-Rodriguez, J., Vizcaino, A., Ebert, C., & Piattini, M. (2010, August). Tools to support global software development processes: a survey. In 2010 5th IEEE International Conference on Global Software Engineering (pp. 13-22). IEEE.
Tian, F., Liang, P., & Babar, M. A. (2022). Relationships between software architecture and source code in practice: An exploratory survey and interview. Information and Software Technology, 141, 106705.
Ozkaya, M., & Erata, F. (2020). Understanding practitioners’ challenges on software modeling: A survey. Journal of Computer Languages, 58, 100963.
Fregnan, E., Baum, T., Palomba, F., & Bacchelli, A. (2019). A survey on software coupling relations and tools. Information and Software Technology, 107, 159-178.
Tavakoli, M., Zhao, L., Heydari, A., & Nenadić, G. (2018). Extracting useful software development information from mobile application reviews: A survey of intelligent mining techniques and tools. Expert Systems with Applications, 113, 186-199.
De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., ... & Ye, P. (2017). Curriculum guidelines for undergraduate programs in data science. Annual Review of Statistics and Its Application, 4, 15-30.
Parry, M. (2018). Data scientists in demand: new programs train students to make honest sense of numbers. The Chronicle of Higher Education.
Song, I. Y., & Zhu, Y. (2017). Big data and data science: Opportunities and challenges of iSchools. Journal of Data and Information Science, 2(3).
Tate, E. (2017). Data Analytics Programs Take Off. Inside Higher Ed.