Open Source, Agile and Reliability Measures

As open source and agile development do work in some circumstances, particularly with regard to shorter and more frequent release policy, we wonder whether the defect profile (reliability growth) found in the open-source projects so far is typical of open-source software development or of software developed iteratively and incrementally. To investigate this, we examined an open source web testing tool developed by an agile leading company. The results of this analysis indicate two findings. First, it supports the tentative findings that iteratively developed software does not exhibit a standard reliability growth in the defect modeling, and second, somewhat surprisingly that the defect density is reducing, as a sign of improving in quality yet the normal measure of software reliability are not useful

Prediksi Reliabilitas Perangkat Lunak Menggunakan Support Vector Regression dan Model Mining

Reliabilitas perangkat lunak didefinisikan sebagai probabilitas operasi perangkat lunak yang bebas dari kegagalan (failure) dalam sebuah periode waktu tertentu. Pemodelan reliabilitas perangkat lunak ini dapat dilakukan salah satunya dengan memanfaatkan data kegagalan perangkat lunak untuk melakukan prediksi kegagalan di masa datang. Salah satu arsitektur yang dipakai dalam pemodelan ini pada umumnya adalah dengan menggunakan beberapa data terakhir untuk melakukan prediksi. Padahal, kegagalan perangkat lunak dapat saja dipengaruhi oleh data yang terdahulu seperti yang telah dibuktikan pada satu penelitian, yang menggunakan teknik model mining untuk memilih data masukan terdahulu tersebut. Pada penelitian ini, diusulkan penggunaan Binary Particle Swarm Optimization (BPSO) sebagai metode model mining untuk melakukan prediksi reliabilitas perangkat lunak dengan menggunakan Support Vector Regression (SVR). Data yang dipakai atau tidak dipakai masing-masing disimbolkan dengan angka “1” atau “0” dan metode ini diujicobakan pada 6 data dari proyek perangkat lunak yang nyata, yaitu data FC1, FC2, FC3, TBF1, TBF2, dan TBF3. Keakuratan model yang diusulkan dibandingkan dengan prediksi yang tidak menggunakan model mining dengan mengukur nilai Mean Squared Error (MSE) dan Average Relative Prediction Error (AE). Metode SVR-BPSO yang diusulkan terbukti dapat menghasilkan prediksi yang lebih akurat, terutama untuk data FC1, FC2, dan FC3 yang bersifat stabil. Sifat data TBF yang berbeda dengan data FC menunjukkan bahwa data ini tidak cocok digunakan sebagai bahan uji coba metode yang diusulkan karena time-between-failure pada data tidak bergantung pada urutan kegagalan tertentu, seperti yang terlihat pada data TBF1, TBF2, dan TBF3. Pemilihan parameter SVR juga mempengaruhi keakuratan prediksi, dimana hal ini dapat diperbaiki pada penelitian selanjutnya. Secara umum, metode yang diusulkan telah dapat menghasilkan prediksi reliabilitas perangkat lunak dengan baik dan penggunaan model mining terbukti dapat memberikan manfaat yang nyata dalam bidang prediksi reliabilitas perangkat lunak. ================================================================= Software reliability is defined as the pobability of failure-free software operation in certain period of time. The modelling of software reliability can be done in one way by using software failure data to predict the future failures. One architecture in this modelling is done generally by using the last few consecutive data to predict the future value, where actually the failure of a software can be dependent also to earlier data as showed in one research about the use of model mining to determine which data to use as prediction. In this research, we propose the use of Binary Particle Swarm Optimization (BPSO) as a model mining method to predict the reliability of software by using Support Vector Regression (SVR) as predictor. To determine which data to use in model mining, the data is symbolized with one “1” or “0” in the structure of BPSO particle. The proposed method is tested with 6 real data from real project, which are called FC1, FC2, FC3, TBF1, TBF2, and TBF3. The accuracy of the proposed model is compared with a predictor without model mining by computing the Mean Squared Error (MSE) and Average Relative Prediction Error (AE). The proposed SVR-BPSO method is proved to be able to predict more accurately, especially in FC1, FC2, and FC3 data which are more stable in nature. The use of TBF data sets proved to be inappropriate as it yields poor prediction results in TBF1, TBF2, and TBF3 data, which may have rooted from the differing nature with FC data. The method to choose SVR parameters can also affect the accuracy of prediction, which opens room for improvement in future research. In general, the proposed method is able to predict the reliability of a software and the use of model mining is important in effort to produce more accurate prediction in software failure data

A Fuzzy Multi Criteria Decision Making Approach To Software Life Cycle Model Selection

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2011Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2011Yazılım, bugünün dünyasında çok geniş bir uygulama alanına sahip ve her türlü iş için bir gereksinim konumundadır. Dolayısıyla, yüksek kalitede yazılım üretmek her türlü iş başarısı için vazgeçilmez bir öneme sahiptir. Yazılım kalitesini sağlamak için, yazılım mühendisliği proje yönetimi yazılım yaşam döngüsünün her aşamasında yer almalıdır. Yazılım mühendisliği proje yönetiminin olmaması yada yeterli olmaması, projelerin zaman, bütçe ve gerekli özellikleri yerine getirememekten dolayı başarısız olmalarına sebep olmaktadır. Diğer yandan, etkin ve verimli yazılım projesi yönetimi halen, yazılım organizasyonları için bir zorluk olarak karşımıza çıkmaktadır. Yazılım mühendisliği proje yönetimi planlama, koordinasyon ve geliştirme aşamalarının kontrolünü gerektirdiğinden, paydaşların memnuniyetini, gereksinimleri ve hedefleri garantileyecek ve yazılım mühendislerine ürün ve geliştirmelerin uygulanmasında önemli kolaylık sağlayacak kararlar verilmelidir. Özetle, karar verme, yazılım geliştirme sürecinde uygulanması gereken süreçlerden biri olmalıdır. Yazılım mühendisliği proje yönetimindeki kritik konulardan birisi de, projenin başarısını etkileyebilecek öneme sahip olan yazılım yaşam döngüsü modeli seçimidir. Yazılım geliştirme sürecinin tamamı seçilen model üzerine kurulduğundan, yazılım yaşam döngüsü modelinin seçimi projenin tüm aşamalarında işgücünün verimli bir şekilde kullanılması açısından vazgeçilmez bir unsurdur. Bulanık kümeler, belirsizliği,kararsızlığı ve insan subjektifliğini temsil etmede en etkin metodlardan birisi olduğundan, bu çalışmada bir bulanık çok kriterli karar verme yaklaşımı önerilmiştir. Bulanık sayılar dilsel ve kesin olmayan verilerin temsilinde kullanılmıştır. Ayrıca, önerilen yaklaşımda, bulanık AHP ve bulanık TOPSIS metodlarının birlikte kullanılması, güvenilir sonuçlar elde etmek ve sonuca mantıklı ve kolay hesaplanabilir bir yoldan gitmek için tercih edilmiştir. Önerilen yaklaşım kullanılarak bir uygulama yapılmıştır. Çalışmanın son bölümünde ise sonuç bölümüne yer verilmiştir.Software is in a wide variety of application areas in todays world and is essential for all kinds of businesses. Developing high quality software for business success is therefore prime importance. For ensuring software quality, software engineering project management needs to be in every stages of the life cycle. Lack of proper and sufficient software engineering project management cause the projects to fail, to have problems with time, budget and required features. However, the establishment of effective and efficient software project management practices still remains a challenge to software organizations. As software engineering project management needs planning, coordinating and controlling of whole development process, many decisions need to be made to guarantee the satisfaction of the stakeholders , requirements and goals, and help software engineers greatly to implement products or applications. In brief, decision making is an essential process that must be used in the software development process. In software engineering project management, one of the critical issues is the selection of the appropriate SLCM, which may affect the success of the project. All the stages of software development process is established due to the model selected, so SLCM selection is sufficient for enabling all the effort be used efficiently in all phases of the project life cycle. A fuzzy multi criteria decision making approach is proposed in the study, since fuzzy sets are inevitable in representing uncertainty, vagueness and human subjectivity. Fuzzy numbers are used for representing linguistic or uncertain data. Moreover, fuzzy AHP and fuzzy TOPSIS are used together in the proposed approach for obtaining reliable results and reaching the result with logical and easy calculations. An application is done using the proposed method and a conclusion is given at the end of the study.Yüksek LisansM.Sc

On the Viability of Quantitative Assessment Methods in Software Engineering and Software Services

IT help desk operations are expensive. Costs associated with IT operations present challenges to profit goals. Help desk managers need a way to plan staffing levels so that labor costs are minimized while problems are resolved efficiently. An incident prediction method is needed for planning staffing levels. The potential value of a solution to this problem is important to an IT service provider since software failures are inevitable and their timing is difficult to predict. In this research, a cost model for help desk operations is developed. The cost model relates predicted incidents to labor costs using real help desk data. Incidents are predicted using software reliability growth models. Cluster analysis is used to group products with similar help desk incident characteristics. Principal Components Analysis is used to determine one product per cluster for the prediction of incidents for all members of the cluster. Incident prediction accuracy is demonstrated using cluster representatives, and is done so successfully for all clusters with accuracy comparable to making predictions for each product in the portfolio. Linear regression is used with cost data for the resolution of incidents to relate incident predictions to help desk labor costs. Following a series of four pilot studies, the cost model is validated by successfully demonstrating cost prediction accuracy for one month prediction intervals over a 22 month period