Analisis Metode Estimasi Biaya pada Perangkat Lunak Beserta Faktor-Faktor yang Mempengaruhi : A Systematic Literature Review

Penulis

  • Amelia Devi Putri Ariyanto Institut Teknologi Sepuluh Nopember, Surabaya
  • Lutfiyatul ‘Azizah Institut Teknologi Sepuluh Nopember, Surabaya
  • Umi Laili Yuhana Institut Teknologi Sepuluh Nopember, Surabaya

DOI:

https://doi.org/10.25126/jtiik.2021864611

Abstrak

Estimasi biaya sampai sekarang masih menjadi salah satu permasalahan utama dalam perencanaan proyek perangkat lunak. Estimasi biaya ini memiliki peran yang penting karena berpengaruh pada berjalannya proyek dan menjadi penentu keberhasilan suatu proyek perangkat lunak. Kegagalan estimasi biaya dalam perencanaan proyek perangkat lunak dapat menyebabkan proyek tidak berjalan dengan baik dan menimbulkan kerugian bagi perusahaan. Oleh karena itu, banyak peneliti sampai saat ini masih mencari dan melakukan penelitian untuk mendapatkan estimasi terbaik. Berbagai metode diusulkan untuk mendapatkan ketepatan akurasi dengan memperhatikan faktor-faktor estimasi biaya. Tujuan penelitian ini adalah membuat Systematic Literature Review (SLR) yang berisi rangkuman dan analisis perkembangan penelitian terbaru tentang estimasi biaya pada perangkat lunak, khususnya pada metode yang digunakan serta faktor-faktor yang mempengaruhi. Penelitian ini berhasil mengkaji 21 penelitian lain dalam lima tahun terakhir (2015-2020) dan didapatkan 24 metode usulan yang terbagi menjadi tiga jenis metode yang sering digunakan dalam melakukan estimasi biaya perangkat lunak yaitu nonparametrik, parametrik dan semiparametrik. Selain itu, penelitian ini juga berhasil menemukan metode dan kombinasi metode terbaik berdasarkan ketepatan akurasi beserta lima faktor utama yang mempengaruhi estimasi biaya sehingga dapat digunakan para peneliti atau praktisi lain untuk mengembangkan estimasi biaya pada proyek perangkat lunak.

 

Abstract

Cost estimation has an important role because it affects the project’s progress and determines the success of a software project. Failure to estimate costs in software project planning can cause the project to not run well and cause losses to the company. Therefore, many researchers are still looking for and researching to get the best estimation by considering the cost estimation factors. The purpose of this study is to create a Systematic Literature Review (SLR) which contains a summary and analysis of the latest research developments on cost estimation in software, especially in the methods used and the factors that affect cost estimation. This study successfully reviewed 21 other studies in the last five years (2015-2020) and obtained 24 planning methods which are divided into three types of methods that are often used in conducting software cost research, namely nonparametric, parametric and semiparametric. Besides, this study also succeeded in finding the best method and combination of methods based on best accuracy, namely COCOMO II and the combination of Genetic Algorithm and Artificial Bee Colony, along with the five main factors that influence cost estimation so that it can be used by researchers or other practitioners to develop cost estimates for software projects.


Downloads

Download data is not yet available.

Referensi

AHMAD, S. F., & SAMAT, P. A, 2018. Extraction Cost of Quality and Testing in Software Project. 2018 IEEE Conference on E-Learning, e-Management and e-Services (IC3e), 109–115.

BAIQUNI, M., SARNO, R., SARWOSRI, & SHOLIQ, 2017. Improving the accuracy of COCOMO II using fuzzy logic and local calibration method. 2017 3rd International Conference on Science in Information Technology (ICSITech), 284–289.

BANIMUSTAFA, A, 2018. Predicting Software Effort Estimation Using Machine Learning Techniques. 2018 8th International Conference on Computer Science and Information Technology (CSIT), 249–256.

BILGAIYAN, S., ADITYA, K., MISHRA, S., & DAS, M, 2018. A Swarm Intelligence based Chaotic Morphological Approach for Software Development Cost Estimation. International Journal of Intelligent Systems and Applications, 11(9), 13.

CHHABRA, S., & SINGH, H, 2016. Simulink based fuzzified COCOMO. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 847–851.

DESAI, V. S., & MOHANTY, R, 2018. ANN-Cuckoo Optimization Technique to Predict Software Cost Estimation. 2018 Conference on Information and Communication Technology (CICT), 1–6.

EL BAJTA, M., IDRI, A., FERNÁNDEZ-ALEMÁN, J. L., ROS, J. N., & TOVAL, A, 2015. Software cost estimation for global software development a systematic map and review study. 2015 International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), 197–206.

FADHIL, A. A., ALSARRAJ, R. G. H., & ALTAIE, A. M, 2020. Software Cost Estimation Based on Dolphin Algorithm. IEEE Access, 8, 75279–75287.

GANDOMANI, T. J., FARAJI, H., & RADNEJAD, M, 2019. Planning Poker in cost estimation in Agile methods: Averaging Vs. Consensus. 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 66–71.

GARG, S., & GUPTA, D, 2015. PCA based cost estimation model for agile software development projects. 2015 International Conference on Industrial Engineering and Operations Management (IEOM), 1–7.

GHAREHCHOPOGH, F. S., MALEKI, I., & TALEBI, A, 2015. Using hybrid model of Artificial Bee Colony and Genetic Algorithms in Software Cost Estimation. 2015 9th International Conference on Application of Information and Communication Technologies (AICT), 102–106.

GHAREHCHOPOGH, F. S., REZAII, R., & ARASTEH, B, 2015. A new approach by using Tabu search and genetic algorithms in Software Cost estimation. 2015 9th International Conference on Application of Information and Communication Technologies (AICT), 113–117.

IZZATI, A. N., & NAJWA, N. F, 2018. Pengaruh Stakeholder Perspective Dalam Penerapan ERP: A Systematic Literature Review. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(1), 41.

KITCHENHAM, B., MENDES, E., & TRAVASSOS, G. H, 2006. A Systematic Review of Cross- vs. Within-Company Cost Estimation Studies.

KURNIAWAN, I., ARMAN, A. A., & MARDIYANTO, S, 2017. Development of analogy-based estimation method for software development cost estimation in government agencies. 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI), 1–6.

LANGSARI, K, & SARNO, R, 2017. Optimizing COCOMO II parameters using particle swarm method. 2017 3rd International Conference on Science in Information Technology (ICSITech), 29–34.

LANGSARI, KHOLED, SARNO, R., & SHOLIQ, 2018. Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method. TELKOMNIKA, 16(5), 2208–2216.

NAGACHARAN, J. G., & MARY, R. R, 2017. Recommendations for Software Pricing Strategies in Different Business Contexts. International Journal of Research in Engineering, IT and Social Sciences, 7(3), 29–33.

PADMAJA, M., & HARITHA, D, 2017. Software Effort Estimation using Meta Heuristic Algorithm. International Journal of Advanced Research in Computer Science, 8(5), 196–201.

PRATAMA, R. Y., SARNO, R., & SHOLIQ, 2017. Optimizing COCOMO II parameters using artificial bee colony method. 2017 11th International Conference on Information Communication Technology and System (ICTS), 125–130.

QASIM, I., TUFAIL, H., & FATIMA, A, 2018. Cost Estimation Techniques for Software Development: A Systematic Literature Review. International Conference on Engineering, Computing & Information Technology (ICECIT 2017), Icecit 2017, 38–42.

SABBAGH JAFARI, S. M., & ZIAADDINI, F, 2016. Optimization of software cost estimation using harmony search algorithm. 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 131–135.

SARNO, R., SIDABUTAR, J., & SARWOSRI, 2015a. Comparison of different Neural Network architectures for software cost estimation. 2015 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 68–73.

SARNO, R., SIDABUTAR, J., & SARWOSRI, 2015b. Improving the accuracy of COCOMO’s effort estimation based on neural networks and fuzzy logic model. 2015 International Conference on Information Communication Technology and Systems (ICTS), 197–202.

SHARMA, B., & PUROHIT, R, 2018. Review of current software estimation techniques. Communications in Computer and Information Science, 799, 380–399.

SINGH, S. P., & KUMAR, A, 2017. Software cost estimation using homeostasis mutation based differential evolution. 2017 11th International Conference on Intelligent Systems and Control (ISCO), 173–181.

SULIMAN, S. M. A., & KADODA, G, 2017. Factors that influence software project cost and schedule estimation. 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT), 1–9.

ULLAH, A., WANG, B., SHENG, J., LONG, J., ASIM, M., & RIAZ, F, 2019. A Novel Technique of Software Cost Estimation Using Flower Pollination Algorithm. 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS), 654–658. 142

ULLAH, M., ALI, R., ABDULLAH, AHMAD, M., KHAN, T., & MULK, F. U, 2020. Software Cost Estimation – A Comparative Study of COCOMO-II and Bailey-Basili Models. 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), 1–5.

WAHONO, R. S, 2007. A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks. Journal of Software Engineering, 1(1), 1–16.

Diterbitkan

31-08-2022

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Analisis Metode Estimasi Biaya pada Perangkat Lunak Beserta Faktor-Faktor yang Mempengaruhi : A Systematic Literature Review. (2022). Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(4), 699-708. https://doi.org/10.25126/jtiik.2021864611