The Impact Of Design Patterns In Refactoring Technique To Measure Performance Efficiency

Designing and developing software application has never been an easy task. The process is often time consuming and requires interaction between several different aspects. It will be harder in re-engineering the legacy system through refactoring technique, especially when consider to achieve software standard quality. Performance is one of the essential a quality attribute of software quality. Many studies in the literature have premise that design patterns improve the quality of object-oriented software systems but some studies suggest that the use of design patterns do not always result in appropriate designs. There are remaining question issues on negative or positive impacts of pattern refactoring in performance. In practice, refactoring in any part or structure of the system may take effect to another related part or structure. Effect of the process using refactoring technique and design patterns may improve software quality by making it more performable efficiency. Considerable research has been devoted in re-designing the system to improve software quality as maintainability and reliability. Less attention has been paid in measuring impact of performance efficiency quality factor. The main idea of this thesis is to investigate the impact, demonstrate how design patterns can be used to refactor the legacy software application in term of performance efficiency. It is therefore beneficial to investigate whether design patterns may influence performance of applications. In the thesis, an enterprise project named SIA (Sistem Informasi Akademik) is designed by applying Java EE platform. Some issues related to design patterns are addressed. The selection of design pattern is based on the application context issue. There are three kind of parameters measure, time behavior, resource utilization and capacity measures that based on standard guideline. We use tools support in experimentation as Apache JMeter and Java Mission Control. These tools provide convenient and generate appropriate result of performance measurement. The experiment results shown that the comparison between the legacy and refactored system that implemented design pattern indicates influence on application quality, especially on performance efficiency. ================================================================================================== Merancang dan mengembangkan aplikasi perangkat lunak bukan merupakan pekerjaan yang mudah karena membutuhkan waktu dan interaksi antara beberapa aspek. Proses desain pada rekayasa ulang akan lebih sulit meskipun melalui teknik refactoring, terutama untuk mencapai standar kualitas perangkat lunak. Kinerja merupakan salah satu atribut terpenting kualitas perangkat lunak. Banyak penelitian menjelaskan pola desain memperbaiki kualitas sistem perangkat lunak berorientasi objek, namun beberapa penelitian juga menunjukkan bahwa penggunaan pola desain tidak selalu menghasilkan desain yang sesuai. Masih ada pertanyaan tentang dampak negatif atau positif dari kinerja pola refactoring. Pada praktiknya, melakukan refactoring pada suatu bagian atau struktur sistem akan berpengaruh pada bagian atau struktur lain yang terkait. Penggunaan teknik refactoring dan pola desain dapat meningkatkan kualitas perangkat lunak dengan kinerja lebih efisien. Sudah banyak penelitian yang berfokus untuk merancang ulang sistem untuk meningkatkan kualitas perangkat lunak sebagai kemampuan rawatan dan keandalan. Tetapi masih kurang penelitian perhatian dalam mengukur dampak faktor kualitas efisiensi kinerja. Tujuan utama dalam tesis ini adalah untuk mengetahui dampaknya, menunjukkan bagaimana pola desain dapat digunakan untuk refactor aplikasi perangkat lunak terdahulu dalam hal efisiensi kinerja. Oleh karena itu, akan bermanfaat untuk menyelidiki apakah pola desain dapat mempengaruhi kinerja aplikasi. Dalam tesis ini, sebuah proyek perusahaan bernama SIA (Sistem Informasi Akademik) dirancang dengan menerapkan platform Java EE. Beberapa masalah yang terkait dengan pola desain diketahui. Pemilihan pola desain berdasarkan pada isu konteks aplikasi. Tiga jenis ukuran parameter dipakai untuk penilitian ini, perilaku waktu, pemanfaatan sumber daya dan ukuran kapasitas yang berdasarkan pada pedoman standar. Kami menggunakan Apache JMeter dan Java Mission Control sebagai alat bantu dalam eksperimen. Hasil percobaan menunjukkan bahwa perbandingan antara sistem terdahulu dengan penelitian ini yang menerapkan pola desain menunjukkan bahwa hasilnya berpengaruh terhadap kualitas aplikasi terutama pada efisiensi kinerja

Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO

The  estimation  of  software  effort  is  an  essential and  crucial   activity   for  the  software   development   life  cycle. Software effort estimation is a challenge that often appears on the project of making a software. A poor estimate will produce result in a worse project management.  Various software cost estimation model has been introduced  to resolve this problem. Constructive Cost Model II (COCOMO II Model) create large extent most considerable  and broadly  used as model  for cost estimation.  To estimate   the  effort  and  the  development   time  of  a  software project,  COCOMO  II model uses cost drivers,  scale factors  and line  of  code.  However,  the  model  is  still  lacking  in  terms  of accuracy both in effort and development  time estimation.  In this study,   we   do   investigate   the   influence   of   components   and attributes to achieve new better accuracy improvement on COCOMO II model. And we introduced the use of Gaussian Membership  Function  (GMF)  Fuzzy  Logic  and Multi-Objective Particle Swarm Optimization method (MOPSO) algorithms in calibrating  and optimizing  the COCOMO  II model parameters. The   proposed   method   is   applied   on   Nasa93   dataset.   The experiment  result of proposed method able to reduce error down to  11.891%  and  8.082%  from  the  perspective  of  COCOMO  II model.  The  method  has  achieved  better  results  than  those  of previous   researches   and  deals  proficient   with  inexplicit   data input and further improve reliability of the estimation method

Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

Estimating the effort and cost of software is an important activity for software project managers. A poor estimate (overestimates or underestimates) will result in poor software project management. To handle this problem, many researchers have proposed various models for estimating software cost. Constructive Cost Model II (COCOMO II) is one of the best known and widely used models for estimating software costs. To estimate the cost of a software project, the COCOMO II model uses software size, cost drivers, scale factors as inputs. However, this model is still lacking in terms of accuracy. To improve the accuracy of COCOMO II model, this study examines the effect of the cost factor and scale factor in improving the accuracy of effort estimation. In this study, we initialized using Particle Swarm Optimization (PSO) to optimize the parameters in a model of COCOMO II. The method proposed is implemented using the Turkish Software Industry dataset which has 12 data items. The method can handle improper and uncertain inputs efficiently, as well as improves the reliability of software effort. The experiment results by MMRE were 34.1939%, indicating better high accuracy and significantly minimizing error 698.9461% and 104.876%

Optimizing Time and Effort Parameters of COCOMO II using Fuzzy Multi-Objective Particle Swarm Optimization

Estimating the efforts, costs, and schedules of software projects is a frequent challenge to software development projects. A bad estimation will result in bad management of a project. Various models of estimation have been defined to complete this estimate. The Constructive Cost Model II (COCOMO II) is one of the most famous models as a model for estimating efforts, costs, and schedules. To estimate the effort, cost, and schedule in project of software, the COCOMO II uses inputs: Effort Multiplier (EM), Scale Factor (SF), and Source Line of Code (SLOC). Evidently, this model is still lack in terms of accuracy rates in both efforts estimated and time of development. In this paper, we introduced to use Gaussian Membership Function (GMF) of Fuzzy Logic and Multi-Objective Particle Swarm Optimization (MOPSO) method to calibrate and optimize the parameters of COCOMO II. It is to achieve a new level of accuracy better on COCOMO II. The Nasa93 dataset is used to implement the method proposed. The experimental results of the method proposed have reduced the error downto 11.89% and 8.08% compared to the original COCOMO II. This method proposed has achieved better results than previous studies

Measuring Performance Efficiency of Application applying Design Patterns and Refactoring Method

Design patterns are always useful concept using in designing and developing a software application. Performance play essential role in the quality attribute of an enterprise application. It is useful to measure and examine how design patterns influence and affect the performance of an application. In this study, we investigate the impact of selected design pattern through refactoring processes for performance efficiency. The systematic study phases included; analyzing, refactoring and performance measuring with implemented in case study SIA system. The performance measuring measure with different test cases and round for the results comparison of each differences test cases and round for design pattern indicate an influence on the performance of an applicatio