Pengukuran Kualitas Website Pengurus Cabang NU Depok Menggunakan Software Metric

Website Pengurus Cabang Nahdlatul Ulama (PCNU) Depok dibuat sebagai sarana penyimpanan dan pengelolaan data dalam organisasi tersebut. Penelitian ini bertujuan untuk mengukur kualitas website PCNU Depok dilihat dari maintainability, flexibility dan testability. Tahapan penelitian yang dilakukan : analisis modul website, instalasi website PCNU, instalasi composer, instalasi PHPMetrics, menjalankan aplikasi PHPMetrics untuk mengukur kualitas website PCNU, menganalisis hasil pengukuran dari PHPMetrics.Penelitian kualitas website dengan metode maintainability index mehasilkan nilai 63,57 yang diklasifikasikan sebagai low maintainability. Low maintainibility index berdampak kepada lemahnya modul website untuk dilakukan perawatan dan pengembangan berdasarkan Lines Of Code, Cyclomatic Complexity, dan Halstead Volume. Faktor flexibility, website mendapatkan klasifikasi moderate atau batas wajar karena memiliki coupling = cohesion. Testability pada website memiliki klasifikasi baik dengan nilai rata-rata 16,65. Dengan nilai testability baik website mendapatkan nilai positip pada proses testing dan maintenance sehingga modul tidak sulit untuk dimengerti dan dikembangkan.

Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.

PENGARUH PENGUJIAN STATIS TERHADAP KUALITAS PERANGKAT LUNAK SITAGIH PADA PT. SEMESTA MITRA SEJAHTERA (SMS)

Sebagai seorang pengembang, menghasilkan suatu produk yang berkualitas tinggi menjadi suatu keharusan. Oleh karena itu, proses pengembangan perangkat lunak perlu ditingkatkan atau diperbaiki untuk meningkatkan kualitas dari perangkat lunak yang dihasilkan. Salah satu tahapan untuk meningkatkan kualitas tersebut adalah dengan melakukan pengujian terhadap perangkat lunak yang dikembangkan. Banyak metode pengujian perangkat lunak yang dapat digunakan, salah satunya adalah pengujian statis. Pengujian ini dapat dilakukan tanpa harus melakukan eksekusi perangkat lunak dan tidak perlu menunggu proses pengembangan selesai dilakukan. Pada penelitian ini, dikembangkan aplikasi Sitagih yang akan diuji pada tiga kategori penerapan, yaitu berdasarkan modul perangkat lunak, komponen perangkat lunak, dan struktur perangkat lunak. Pengujian pada komponen perangkat lunak dilakukan dengan mengukur Software Metric menggunakan aplikasi PhpMetrics. Berdasarkan hasil pengujian diperoleh hasil bahwa masih terdapat beberapa Class yang belum lolos kasus uji. Selain itu, hasil pengukuran metric diperoleh tingkat maintainability yang rendah sehingga aplikasi Sitagih masih perlu perbaikan terutama di dokumentasi, penerapan algoritma yang lebih sederhana, dan penambahan sub kelas. Khususnya pada Class Order yang memiliki nilai maintainability di bawah 65 dan termasuk ke dalam kategori sulit untuk dirawat dan dikembangkan. Dari penelitian ini, dapat dibuktikan bahwa pengujian statis memiliki pengaruh yang cukup signifikan terhadap kualitas perangkat lunak yang dihasilkan dari pengembangan.

Literature review on software metrics and a New Proposal

The use of metrics is important in software development activities as they make it possible to check quality, identify failures and other benefits. The objective of this paper is to propose a new software metric based on a bibliometric study and a literature review on software metrics. The bibliometric research was carried out in the Scopus and Web of Science databases to identify the distribution of articles by year of publication, the main authors, affiliation, country, the most common languages, the types of documents, journals with more publications, areas of knowledge, and the keyword clusters. Twenty-three articles were subsequently selected for reading to compose the literature review. The results of the bibliometric research show that (i) there is no defined core of research; (ii) there is a fluctuation of the number of published articles; (iii) the predominant language is English, and the country with the highest index of publications is the United States; (iv) the main area of knowledge is computer science; (v) in relation to affiliation, Florida Atlantic University stands out; (vi) the journal with the largest number of publications is the Journal of Systems and Software. The literature review showed that many software metrics can be used for different purposes, but most of them are related to code, and none are related to acceptance. As such, a support metric for the software acceptance process is proposed to facilitate the delivery phase of the software product, providing security for the customer and cost savings for the developing company.

Internal Quality Evolution of Open-Source Software Systems

The evolution of software is necessary for the success of software systems. Studying the evolution of software and understanding it is a vocal topic of study in software engineering. One of the primary concepts of software evolution is that the internal quality of a software system declines when it evolves. In this paper, the method of evolution of the internal quality of object-oriented open-source software systems has been examined by applying a software metric approach. More specifically, we analyze how software systems evolve over versions regarding size and the relationship between size and different internal quality metrics. The results and observations of this research include: (i) there is a significant difference between different systems concerning the LOC variable (ii) there is a significant correlation between all pairwise comparisons of internal quality metrics, and (iii) the effect of complexity and inheritance on the LOC was positive and significant, while the effect of Coupling and Cohesion was not significant.

Software Fault Estimation Tool Based on Object-Oriented Metrics

A fault is an error that has effects on system behaviour. A software metric is a value that represents the degree to which software processes work properly and where faults are more probable to occur. In this research, we study the effects of removing redundancy and log transformation based on threshold values for identifying faults-prone classes of software. The study also contains a comparison of the metric values of an original dataset with those after removing redundancy and log transformation. E-learning and system dataset were taken as case studies. The fault ratio ranged from 1%-31% and 0%-10% for the original dataset and 1%-10% and 0%-4% after removing redundancy and log transformation, respectively. These results impacted directly the number of classes detected, which ranged between 1-20 and 1-7 for the original dataset and 1-7 and 0-3) after removing redundancy and log transformation. The Skewness of the dataset was deceased after applying the proposed model. The classified faulty classes need more attention in the next versions in order to reduce the ratio of faults or to do refactoring to increase the quality and performance of the current version of the software.

A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

Maintainability index (MI) is a software metric that offers measurements of the maintainability before release of the software by facilitating several substantial features of the system. In general, there is a common formula for determining the MI for all the software metrics to ensure the system's reliability. As it does not provide appropriate results regarding the reliability of the system, it is essential to focus on the next level of MI of software. Hence, this paper intends to allot an optimal weight and a constant to each software metric, which is optimized by grey wolf optimization (GWO). As a result, it can provide a new variant of MI by proposed enhanced model-GWO (EM-GWO). This optimized MI can ensure the efficiency of the respective software in such a way that it can provide an enhanced score from the system. Further, the proposed method is compared with conventional models such as enhanced model-generic algorithm (EM-GA), EM-particle swarm optimization (PSO), EM-ant bee colony (ABC), EM-differential evolution (DE), and EM-fire fly (FF), and the results are obtained.

Metrics Based Feature Selection for Software Defect Prediction

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.

Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.