ABSTRACT
Effort estimation plays an important role in the software development process by supporting the decision-making process for the stakeholders. DevOps has become a widely used software engineering practice with the collaboration of the development and operational teams. This paper addresses the factors that affect the effort estimation strategies and practices in DevOps based software development in Sri Lanka. This study explains the research approach, generation of the conceptual model and the quantitative data analysis process in detail. A survey is conducted among the software professionals who are working in DevOps-based software development in the Sri Lanka IT industry and a detailed data analysis is performed using statistical techniques to identify the reliability, correlation and significance of the considered factors. With an extensive analysis the independent variables namely, exploration, communication, and technology stack are identified as highly impacted factors to the effort estimation in DevOps-based software development. We also provide recommendations for the effort estimation strategies and practices; hence the managerial decision can be made for the improvements of the development process.
- El Bajta, M. (2015). Analogy-based software development effort estimation in global software development. 10th International Conference on Global Software Engineering Workshops (ICGSEW), pp. 51–54. DOI: 10.1109/ICGSEW.2015.19Google ScholarDigital Library
- Phannachitta, P. (2018). Robust comparison of similarity measures in analogy-based software effort estimation. International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA). Malabe, Sri Lanka, pp. 1-7. DOI: 10.1109/SKIMA.2017.8294126Google Scholar
- Lwakatare, L. E., Kuvaja, P., & Oivo, M. (2016). An Exploratory Study of DevOps Extending the Dimensions of DevOps with Practices. 11th International Conference on Software Engineering Advances (ICSEA), pp. 91–99.Google Scholar
- Rubasinghe, I., Meedeniya, D., & Perera, I. (2018). Automated Inter-artefact Traceability Establishment for DevOps Practice. 17th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2018), Singapore, pp. 211-216. DOI: 10.1109/ICIS.2018.8466414Google Scholar
- Rubasinghe, I., Meedeniya, D., & Perera, I. (2018). Traceability Management with Impact Analysis in DevOps based Software Development. International Conference on Advances in Computing, Communications, and Informatics (ICACCI), pp. 1956–1962. DOI: 10.1109/ICACCI.2018.8554399Google ScholarCross Ref
- Palihawadana, S., Wijeweera, C. H., Sanjitha, M. G. T. N., Liyanage, V. K., , Perera, I., & Meedeniya, D. (2017). Tool support for traceability management of software artefacts with DevOps practices. Moratuwa Engineering Research Conference (MERCon), Colombo, Sri Lanka, pp. 129-134. DOI: 10.1109/MERCon.2017.7980469Google ScholarCross Ref
- Meedeniya, D., Rubasinghe, I., & Perera, I. (2019). Software Artefacts Consistency Management towards Continuous Integration: A Roadmap. International Journal of Advanced Computer Science and Applications, 10(4), 100-110. 10.14569/IJACSA.2019.0100411Google Scholar
- Rubasinghe, I., Meedeniya, D., & Perera, I. (2020). Tool Support for Software Artefact Traceability in DevOps Practice: SAT-Analyser. Pendyala, V., (Eds)., in Tools and Techniques for Software Development in Large Organizations, ch. 5, 130-167. Hershey, PA: IGI Global. DOI: 10.4018/978-1-7998-1863-2.ch005Google ScholarCross Ref
- Rubasinghe, I., Meedeniya, D., & Perera, I. (2017). Towards Traceability Management in Continuous Integration with SAT-Analyzer. 3rd International Conference on Communication and Information Processing (ICCIP 2017), Tokyo, Japan, pp. 77-81. DOI: 10.1145/3162957.3162985Google ScholarDigital Library
- Meedeniya, D., Rubasinghe, I., & Perera, I. (2020). Artefact Consistency Management in DevOps Practice: A Survey. Pendyala, V., (Eds)., in Tools and Techniques for Software Development in Large Organizations, ch. 4, 98-129. Hershey, PA: IGI Global. DOI: 10.4018/978-1-7998-1863-2.ch004Google ScholarCross Ref
- Erich, F. M. A., Amrit, C., & Daneva, M. (2017). A qualitative study of DevOps usage in practice. Journal of Software: Evolution and Process, 29(6), 1–20. DOI: 10.1002/smr.1885Google ScholarDigital Library
- Riungu-Kalliosaari, L., Mäkinen, S., Lwakatare, L. E., Tiihonen, J., & Männistö, T. (2016). DevOps adoption benefits and challenges in practice: A case study. International Conference on Product-Focused Software Process Improvement (PROFES). LNCS 10027, 590–597. DOI: 10.1007/978-3-319-49094-6_44Google ScholarCross Ref
- Pérez, J. F., Wang, W., & Casale, G. (2015). Towards a DevOps approach for software quality engineering. ACM/SPEC Workshop on Challenges in Performance Methods for Software Development (WOSP-C), pp.5–10. DOI: 10.1145/2693561.2693564Google ScholarDigital Library
- Meedeniya, D., Rubasinghe, I., & Perera, I. (2019). Traceability Establishment and Visualization of Software Artefacts in DevOps Practice: A Survey. International Journal of Advanced Computer Science and Applications (IJACSA), 10(7), 66 - 76. DOI: 10.14569/IJACSA.2019.0100711Google Scholar
- Senapathi, M., Buchan, J., & Osman, H. (2018). DevOps capabilities, practices, and challenges: Insights from a case study. 22nd International Conference on Evaluation and Assessment in Software Engineering, pp. 57–67, DOI: 10.1145/3210459.3210465Google ScholarDigital Library
- Chung, S. (2017). Object-oriented programming with DevOps. 18th Annual Conference on Information Technology Education (SIGITE), 65. 10.1145/3125659.3125670Google ScholarDigital Library
- Menzies, T., Chen, Z., Hihn, J., & Lum, K. (2006). Selecting best practices for effort estimation. IEEE Transactions on Software Engineering, 32(11), 883–895. DOI: 10.1109/TSE.2006.114Google ScholarDigital Library
- Britto, R., Mendes, E., & Wohlin, C. (2016). A specialized global software engineering taxonomy for effort estimation. 11th IEEE International Conference on Global Software Engineering (ICGSE), PP. 154–163. DOI: 10.1109/ICGSE.2016.11Google ScholarCross Ref
- Fávero, E. M. D. B., Pereira, R., Pimentel, A. R., & Casanova, D. (2018). Analogy-based Effort Estimation: A Systematic Mapping of Literature. Infocomp, 17(2), 7–22.Google Scholar
- Khan, K., & Araghinejad, S. (2010). The Evaluation of Well-known Effort Estimation Models based on Predictive Accuracy Indicators. Measurement, 213–251. DOI: 10.1007/978-94-007-7506-0_7Google Scholar
- Idri, Ali, Abnane, I., & Abran, A. (2018). Support vector regression-based imputation in analogy-based software development effort estimation. Journal of Software: Evolution and Process, 30(12), 1–23. DOI: 10.1002/smr.2114Google ScholarDigital Library
- Hemon, A., Fitzgerald, B., Lyonnet, B., & Rowe, F. (2020). Innovative Practices for Knowledge Sharing in Large-Scale DevOps. IEEE Software, 37(3), 30–37. DOI: 10.1109/MS.2019.2958900Google ScholarCross Ref
- Forsgren, N., & Kersten, M. (2018). DevOps metrics. Communications of the ACM, 61(4), 44–48. DOI: 10.1145/3159169Google ScholarDigital Library
- Perera, P., Bandara, M., & Perera, I. (2017). Evaluating the impact of DevOps practice in Sri Lankan software development organizations. 16th International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 281–287. DOI: 10.1109/ICTER.2016.7829932Google Scholar
- Garusinghe, A., Perera, I., & Meedeniya, D. (2017). Service oriented product lines - managed service level agreements for better quality of service. International Journal on Advances in ICT for Emerging Regions (ICTer), 10(2), 1-11. DOI: 10.4038/icter.v10i2.7184Google ScholarCross Ref
- Debbiche, F. Wrang, M., & Sinkala, K. (2019). Accelerating Software Delivery in the context of Requirements Analysis and Breakdown for DevOps: A multiple-case study Bachelor. Thesis, University of Gothenburg.Google Scholar
- Chen, B. (2019). Improving the software logging practices in DevOps. IEEE/ACM 41st International Conference on Software Engineering: Companion, pp. 194–197. DOI: 10.1109/ICSE-Companion.2019.00080Google ScholarDigital Library
- Leite, L., Rocha, C., Kon, F., Milojicic, D., & Meirelles, P. (2019). A survey of DevOps concepts and challenges. ACM Computing Surveys, 52(6). DOI: 10.1145/3359981Google ScholarDigital Library
- Díaz, J., Almaraz, R., Pérez, J., & Garbajosa, J. (2018). DevOps in practice - An exploratory case study. ACM International Conference Proceeding Series, Part F1477, 18–20. DOI: 10.1145/3234152.3234199Google ScholarDigital Library
- Jones, S., Noppen, J., & Lettice, F. (2016). Management challenges for DevOps adoption within UK SMEs. 2nd International Workshop on Quality-Aware DevOps (QUDOS), pp. 7–11. DOI: 10.1145/2945408.2945410Google ScholarDigital Library
- Taherdoost, H. (2017). Determining sample size; How to calculate the survey sample size. International Journal of Economics and Management Systems, 2(2), 237–239. DOI: http://www.iaras.org/iaras/journals/ijemsGoogle Scholar
- Tennakoon, T. M. (2020). Effort Estimation Strategies and Practices of DevOps Based Software Development In Sri Lanka, Survey form. [online: https://docs.google.com/forms/d/e/1FAIpQLSeXJV7wUyC0MTbrzwazNp5dKulkxRDQ2iV02Gl4GlvEh0aVtg/viewform?usp=sf_link]Google Scholar
- Adom, D., & Hussain, E. K. and Joe, A. (2018). Theoretical and Conceptual Framework: Mandatory Ingredients of a Quality Research. International Journal of Scientific Research, 7(1), 93–98.Google Scholar
- Jilcha Sileyew, K. (2020). Research Design and Methodology. In Cyberspace, pp. 1–12. DOI: 10.5772/intechopen.85731Google ScholarCross Ref
- ICTA. (2019). National IT-BPM Workforce Survey 2019. National IT - Bpm Workforce Survey, https://nvq.gov.lk/LMI_Bulletin/2019_Vol_I/files/basic-html/page1.html.Google Scholar
- Perera, P., Silva, R., & Perera, I. (2017). Improve software quality through practising DevOps. 17th International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 13–18. DOI: 10.1109/ICTER.2017.8257807Google Scholar
- de Smith, M. J. (2018). Statistical Analysis Handbook: A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools, The Winchelsea Press, Drumlin Security Ltd, Edinburgh.Google Scholar
- Soh, J., Singh, P. (2020). Machine Learning Operations. In: Data Science Solutions on Azure. Apress, Berkeley, CA, 259-279.Google ScholarCross Ref
- Karamitsos, I., Albarhami, S., Apostolopoulos, C. (2020). Applying DevOps Practices of Continuous Automation for Machine Learning. Information. 11(7):363. DOI: 10.3390/info11070363Google ScholarCross Ref
- Impact Factors and Best Practices to Improve Effort Estimation Strategies and Practices in DevOps
Recommendations
Best managerial practices in agile development
ACM SE '14: Proceedings of the 2014 ACM Southeast Regional ConferenceAgile development has been gaining momentum over the year. It practices are perceived by some to be the best for software development. This work investigates agile best development and managerial practices, specially the benefits for optimizing the ...
Selection of DevOps best test practices: A hybrid approach using ISM and fuzzy TOPSIS analysis
AbstractTesting is a complex phase in DevOps process due to need of an automated process that provides feedback at different strategies of continuous development and operations pipeline. Software organization face several challenges during the testing ...
- Identified the best test practices for DevOps process.
- Empirically validate the best test practices for DevOps process.
- Presented a holistic view of the best test practices to assist practitioners to revise and develop new strategies of testing for the ...
Assessing the Maturity of DevOps Practices in Software Industry: An Empirical Study of HELENA2 Dataset
EASE '22: Proceedings of the 26th International Conference on Evaluation and Assessment in Software EngineeringCurrently, the software development organizations are adopting DevOps practices in order to develop quality product. Due to the lack of definition of DevOps, the principles, practices, and methods adopted in DevOps to determine success have changed ...
Comments