Evaluasi dan Perbaikan Kualitas Desain Diagram Kelas

Dalam proses pengembangan dan pemeliharaan proyek perangkat lunak, kualitas merupakan salah satu hal penting yang menjadi penentu keberhasilan perangkat lunak.Kesalahan yang tidak ditemukan pada awal pengembangan akan membutuhkan sumber daya, biaya, dan waktu perbaikan yang lebih tinggi. Salah satu tahapan yang dilakukan saat proses pengembangan perangkat lunak adalah pemodelan data. Pada perangkat lunak yang berorientasi objek, data biasanya dimodelkan dalam bentuk diagram kelas. Kualitas pada diagram kelas sangat tergantung pada pengetahuan dari perancang. Oleh karena itu, berbagai metrik telah dikembangkan untuk menilai kualitas desain dari berbagai aspek. Pada paper ini, Penulis mengusulkan sebuah pendekatan dan model untuk mengevaluasi, mendeteksi, dan memperbaiki desain kelas diagram, sehingga sesuai dengan kriteria kualitas yang diharapkan

Evaluasi dan Perbaikan Kualitas Desain Diagram Kelas

Dalam proses pengembangan dan pemeliharaan proyek perangkat lunak, kualitas merupakan salah satu hal penting yang menjadi penentu keberhasilan perangkat lunak.Kesalahan yang tidak ditemukan pada awal pengembangan akan membutuhkan sumber daya, biaya, dan waktu perbaikan yang lebih tinggi. Salah satu tahapan yang dilakukan saat proses pengembangan perangkat lunak adalah pemodelan data. Pada perangkat lunak yang berorientasi objek, data biasanya dimodelkan dalam bentuk diagram kelas. Kualitas pada diagram kelas sangat tergantung pada pengetahuan dari perancang. Oleh karena itu, berbagai metrik telah dikembangkan untuk menilai kualitas desain dari berbagai aspek. Pada paper ini, Penulis mengusulkan sebuah pendekatan dan model untuk mengevaluasi, mendeteksi, dan memperbaiki desain kelas diagram, sehingga sesuai dengan kriteria kualitas yang diharapkan

Hybrid Parameter Optimization Approach with Adaptive Neuro Fuzzy Inference System for the Software Maintainability

This paper presents a novel method to measure the maintainability of the software from the design artifact. It is an inevitable measure because it aims to attain software with a better quality. The system is designed to measure the maintainability of the system from the UML class metric. This is extracted from the UML class diagram to predict the maintainability of the class diagram. The system is implemented using CFS from the Weka tool to select an optimized variable from a set of variables i.e UML class metric. Hybrid ANFIS is an artificial intelligence technique which has been incorporated with the optimizing algorithms to reduce the overall number of UML metric and build a Fuzzy Inference System (FIS) based on the learning process. The optimization attains an enhanced result since it is done continually by both using feature selection and optimization algorithms repetitively, which results in reducing the UML metric considerably to measure the maintainability of the software. The proposed research work is evaluated in terms of the performance measures, MSE, RMSE, true positive rates and the result is clearly shown that a better optimization of the maintainability measure estimation process can be done

Software evolvability - empirically discovered evolvability issues and human evaluations

Evolution of a software system can take decades and can cost up to several billion Euros. Software evolvability refers to how easily software is understood, modified, adapted, corrected, and developed. It has been estimated that software evolvability can explain 25% to 38% of the costs of software evolution. Prior research has presented software evolvability criteria and quantified the criteria utilizing source code metrics. However, the empirical observations of software evolvability issues and human evaluations of them have largely been ignored. This dissertation empirically studies human evaluations and observations of software evolvability issues. This work utilizes both qualitative and quantitative research methods. Empirical data was collected from controlled experiments with student subjects, and by observing issues that were discovered in real industrial settings. This dissertation presents a new classification for software evolvability issues. The information provided by the classification is extended by the detailed analysis of evolvability issues that have been discovered in code reviews and their distributions to different issue types. Furthermore, this work studies human evaluations of software evolvability; more specifically, it focuses on the interrater agreement of the evaluations, the affect of demographics, the evolvability issues that humans find to be most significant, as well as the relationship between human evaluation and source code metrics based evaluations. The results show that code review that is performed after light functional testing reveals three times as many evolvability issues as functional defects. We also discovered a new evolvability issue called "solution approach", which indicates a need to rethink the current solution rather than reorganize it. For solution approach issues, we are not aware of any research that presents or discusses such issues in the software engineering domain. We found weak evidence that software evolvability evaluations are more affected by a person's role in the organization and the relationship (authorship) to the code than by education and work experience. Comparison of code metrics and human evaluations revealed that metrics cannot detect all human found evolvability issues