Efficient Indicators to Evaluate the Status of Software Development Effort Estimation inside the Organizations

Development effort is an undeniable part of the project management which considerably influences the success of project. Inaccurate and unreliable estimation of effort can easily lead to the failure of project. Due to the special specifications, accurate estimation of effort in the software projects is a vital management activity that must be carefully done to avoid from the unforeseen results. However numerous effort estimation methods have been proposed in this field, the accuracy of estimates is not satisfying and the attempts continue to improve the performance of estimation methods. Prior researches conducted in this area have focused on numerical and quantitative approaches and there are a few research works that investigate the root problems and issues behind the inaccurate effort estimation of software development effort. In this paper, a framework is proposed to evaluate and investigate the situation of an organization in terms of effort estimation. The proposed framework includes various indicators which cover the critical issues in field of software development effort estimation. Since the capabilities and shortages of organizations for effort estimation are not the same, the proposed indicators can lead to have a systematic approach in which the strengths and weaknesses of organizations in field of effort estimation are discovered.Comment: 10 page

Meningkatkan Akurasi Perkiraan Waktu Proyek Perangkat Lunak Dalam COCOMO II Dengan Mengubah Nilai Parameter

Good management of software projects can be obtained through accurate time estimates. When estimating less accurate time it will affect the lack of effective management of the software project and the entire process during project development. Barry Boehm, an inventor of COCOMO, has developed the COCOMO 1 cost driver that has an effect on the accuracy of the estimated time results. But if you only use the COCOMO II cost driver, it is still far from the accuracy of the desired results. Therefore it is necessary to change the values of parameters C and D for estimated time. Changes in parameter values are done by decreasing the initial gradation by 0,1 so that the approximate results become more optimal and close to the original values. Based on the implementation of the proposed method, the results show that the error decreases to% when compared to using only the COCOMO I and COCOMO II cost drivers without changing parameter values. So that the accuracy of the estimated project time can increase

Peningkatan Akurasi Estimasi Usaha Dan Biaya Perangkat Lunak Pada Cocomo II Berdasarkan Model Logika Fuzzy Gaussian Dan Bee Colony Optimization

Pengembangan perangkat lunak merupakan proses yang tidak sepenuhnya sempurna. Masih sering terdapat kegagalan hingga ditolaknya suatu proyek perangkat lunak. Salah satu faktor penting yang mempengaruhi keberhasilan proyek yaitu perkiraan usaha dan biaya. Perkiraan usaha dan biaya yang akurat akan memberikan manajemen yang baik untuk proyek perangkat lunak. Apabila perkiraan usaha dan biaya kurang akurat maka akan mempengaruhi manajemen proyek perangkat lunak dan kurang efektifnya proses pengembangan proyek tersebut. Dalam beberapa dekade terakhir industry pembuatan perangkat lunak telah diperkenalkan dengan model estimasi COCOMO II. Penambahan cost driver COCOMO II yang diperkenalkan Barry Boehm pada tahun 2000 digunakan dalam penulisan ini guna memberikan hasil akurasi yang lebih baik karena telah mencakup keseluruhan bagian yang di estimasi. Namun berdasarkan penelitian, akurasi hasil perkiraan usaha dan biaya dengan metode COCOMO II Fuzzy Gaussian masih jauh dari Actual Effort. Oleh sebab itu peningkatan akurasi dari hasil COCOMO II Fuzzy Gaussian masih dapat dilakukan dengan menggunakan metode Bee Colony Optimization yang dapat menghasilkan optimasi yang lebih baik, terlihat dari hasil MMRE loyal. Selain itu penggunaan metode Bee Colony Optimization dapat meningkatkan akurasi yang dihasilkan, meminimalkan error antara estimasi biaya dengan nilai yang sebenarnya. Penulisan ini tidak hanya sebatas menerapkan metode Bee Colony Optimization saja, tetapi juga melakukan perubahan nilai parameter A dan B pada COCOMO II dengan gradual awal adalah 0,01 untuk mencapai nilai optimal pada gradual tertentu. Perubahan nilai pada parameter A dan B dilakukan dengan cara menaikkan dan menurunkan rentang nilai dengan gradual yang telah ditentukan. Apabila hasil optimal telah didapatkan maka pencarian total nilai kesalahan (MMRE loyal) dihentikan. Berdasarkan hasil implementasi dari metode yang diusulkan pada penulisan ini, kesalahan akurasi perkiraan usaha dan biaya proyek perangkat lunak dapat turun 38% bila dibandingkan penelitian sebelumnya. Dengan demikian metode yang diusulkan membuktikan bahwa kesalahan dalam melakukan perkiraan usaha dan biaya perangkat lunak semakin berkurang dan mendekati dengan nilai yang sebenarnya. Sehingga, akurasi estimasi usaha dan biaya proyek perangkat lunak dapat ditingkatkan. ================================================================= Software development is a process that is not completely perfect. There are still frequent failures until the rejection of a software project. One of the important factors affecting project success is business and cost estimation. Accurate business and cost estimates will provide good management for software projects. If the business and cost estimates are less accurate then it will affect the management of the software project and the ineffectiveness of the project development process. In the last few decades software industry has been introduced with COCOMO II estimation model. The addition of the COCOMO II cost driver introduced by Barry Boehm in 2000 is used in this paper to provide better accuracy results as it covers the entire section in the estimation. However, based on the research, the accuracy of business and cost estimates using COCOMO II Fuzzy Gaussian method is still far from Actual Effort. Therefore, the increased accuracy of COCOMO II Fuzzy Gaussian results can still be done using Bee Colony Optimization method which can result in better optimization, seen from MMRE loyal results. In addition, the use of the Bee Colony Optimization method can improve the resulting accuracy, minimizing the error between estimated cost and actual value. This is not only to apply the Bee Colony Optimization method, but also to change the values of parameters A and B on COCOMO II with the initial gradual is 0.01 to achieve optimal value on a certain gradual. The change of values on parameters A and B is done by raising and lowering the range of values with a predetermined gradual. If optimal results have been obtained then the total search error value (MMRE loyal) is stopped. Based on the results of the implementation of the proposed method at this writing, the accuracy error of business estimates and software project costs may fall by 38% when compared to previous studies. Thus the proposed method proves that mistakes in making business estimates and software costs are diminishing and closer to their true value. Thus, the accuracy of business estimates and software project costs can be increased