Preliminary Measurements in Identifying Design Flaws

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

The role of design pattern in the form of software metric and internal code architecture for object-oriented design plays a critical role in software en-gineering in terms of production cost efficiency. This paper discusses about code reusability that is a frequently exercised cost saving methodology in IT produc-tion. After reviewing existing literatures towards study on software metrics, we found that very few studies are witnessed to incline towards code reusability. Hence, we developed a simple analytical model that establishes relationship between the design components of standard software metric and code reusability using case studies of three software projects (Customer Relationship Management project, Supply Chain Management project, and Enterprise Relationship Management project). We also testify our proposal using stochastic based Markov model to find that proposed system can extract significant information of maximized values of code reusability with increasing level of uncertainties of software project methodologies.

The role of the design pattern in the form of software metric and internal code architecture for object-oriented design plays a critical role in software engineering regarding production cost efficiency. This paper discusses code reusability that is a frequently exercised cost saving methodology in IT production. After reviewing existing literature towards a study on software metrics, we found that very few studies are witnessed to incline towards code reusability. Hence, we developed a simple analytical model that establishes a relationship between the design components of standard software metric and code reusability using case studies of three software projects (Customer Relationship Management project, Supply Chain Management project, and Enterprise Relationship Management project). We also testify our proposal using stochastic based Markov model to find that proposed system can extract significant information of maximized values of code reusability with increasing level of uncertainties of software project methodologies.

Design defects come from poor design choices and have the effect of degrading the quality of object-oriented designs.Therefore, they present opportunities for improvements.However, design defects have not been precisely specified and there are few appropriate tools that allow their detection as well as their correction. Our goal is to provide a systematic method to specify systematically design defects precisely and to generate automatically detection and correction algorithms from their specifications. The detection algorithms are based not only on metrics but also on semantical and structural properties whereas the correction algorithms are based on refactorings. We apply and validate these algorithms on open-source object-oriented programs to show that our method allows a systematic specification, a precise detection, and a suitable correction of design defects.

Economic indicators in economic policies have an important place in determining the levels of development. Determining and classifying the existing social and economic structures of countries is very important for examining the development states and possible development tendencies of countries and forming regional development policies in line with these. The aim in cluster analysis, is to classify datas in to similarity and perform useful knowledge for the researcher. Cluster analysis, which became more popular among the subjects of statistical classification in recent years, can give more reliable results when there is apriori knowledge about number of clusters. Fuzzy models interested in fuzzy model structures and try to estimate system behaviours that has no knowledge about their structure. Fuzzy Cluster Analysis is try to decompose the groups which membership degrees cannot be determined. When the number of datas and variables increased or cluster structures came to closer for all, Cluster analysis has given more successful results then the other cluster analysis methods. In this study, Turkish Republics were classified in terms of the indicators determined by using Fuzzy C-Means (FCM) and Gath Geva methods which are frequently used in fuzzy clustering analysis. The objective was to find out the common class structures of Turkish Republics which came out with the disintegration of the Soviet Union in 1991 and which experienced economic similar problems and thus to help countries in the same clusters in similar economic planning. Results are also compared between fuzzy and crisp clustering analysis methods.

Software metrics serve as a significant issue to improve software quality. It is an important research of software engineering. In line with the methodologies of object-oriented analysis and design widely developed, many software metrics techniques have been proposed. However, not many focus on the metrics evaluation of an inheritance hierarchy. In this paper, we propose a novel concept named unit repeated inheritance (URI) in Z notation to realize object-oriented software metrics. The approach describes an inheritance level technique (ILT) method as a guide to measure the software complexity of an inheritance hierarchy. The measures of inheritance metrics are formed based on the proposed mechanism. Also, we use Lex and Yacc to construct a windowing tool which is used in conjunction with a conventional C++ programming environment to assist a programmer to analyze and measure his/her C++ programs.

The ability to predict which modules are fault-prone can assist in directing quality enhancement efforts to modules at risk. This is especially critical for high-assurance systems where software failures may have severe consequences. While the field of defect rediction based on software metrics is mature, no study has addressed the use of metrics for predicting faults related to exception handling. In this paper, we propose exception-based software metrics that are based on the structural attributes of exception handling call graphs. We empirically validate the proposed metrics through a case study of Hadoop Core using data mined from software repositories and defect reports. The results of our case study are comparable to the results of other software metric studies in the literature. We also show that our exception-based software metrics can be better predictors of fault-proneness of exception classes compared to conventional software metrics.

Design defects are one of the main reasons for the decline of software design quality. Effective detection of design defects plays an important role in improving software maintainability and scalability. On the basis of defining software design defects, according to C&K design metrics and heuristics, this paper extracts the relevant features of design defects. Based on classical machine learning methods, classifiers are trained for design defect, and candidate designs are classified by classifiers, so as to identify whether there is a design defect in the design. Experiments show that the method has high accuracy and recall rate in identifying design defects.

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in...

Innovative education is the basic education mode of innovative talent cultivation, and it's also the urgent needs of vocational college application-oriented talents.According to the the concept of innovation education, the higher vocational colleges cognition to the generative P.E.teaching, it stimulates the students' creative thinking in the view of generative thinking and constructs the fuzzy matrix which focus on generative teaching target attribution, at this degree, based on the associate membership to determine the classification relationship of P.E.teaching, that will timely feedback and fuzzy cluster analyze the generative interactive state in the P.E.teaching, further through the data mining to adjust the teaching idea and teaching behavior, it continuously breaks through the old mode of physical education and constructs P.E.teaching mode of innovation mechanism, providing theory basis and the practice path for the P.E.teaching in higher vocational colleges to a certain extent.

A Framework for Managing, Measuring, and Predicting Attributes of Software Development Products and Processes Reflecting the immense progress in the development and use of software metrics in the past decades, Software Metrics: A Rigorous and Practical Approach, Third Edition provides an up-to-date, accessible, and comprehensive introduction to software metrics. Like its popular predecessors, this third edition discusses important issues, explains essential concepts, and offers new approaches for tackling long-standing problems. New to the Third Edition This edition contains new material relevant to object-oriented design, design patterns, model-driven development, and agile development processes. It includes a new chapter on causal models and Bayesian networks and their application to software engineering. This edition also incorporates recent references to the latest software metrics activities, including research results, industrial case studies, and standards. Suitable for a Range of Readers With numerous examples and exercises, this book continues to serve a wide audience. It can be used as a textbook for a software metrics and quality assurance course or as a useful supplement in any software engineering course. Practitioners will appreciate the important results that have previously only appeared in research-oriented publications. Researchers will welcome the material on new results as well as the extensive bibliography of measurement-related information. The book also gives software managers and developers practical guidelines for selecting metrics and planning their use in a measurement program.

Class noise detection based on software metrics and ROC curves

Different challenges arise while detecting deficient software source code. Usually a large number of potentially problematic entities are identified when an individual software metric or individual quality aspect is used for the identification of deficient program entities. Additionally, a lot of these entities quite often turn out to be false positives, i.e., the metrics indicate poor quality whereas experienced developers do not consider program entities as problematic. The number of entities identified as potentially deficient does not decrease significantly when the identification of deficient entities is carried out by applying code smell detection rules. Moreover, the intersection of entities identified as allegedly deficient among different code smell detection tools is small, which suggests that the implementation of code smell detection rules are not consistent and uniform. To address these challenges, we present a novel approach for identifying deficient entities that is based on applying the majority function on the combination of software metrics. Program entities are assessed according to selected quality aspects that are evaluated with a set of software metrics and corresponding threshold values derived from benchmark data, considering the statistical distributions of software metrics values. The proposed approach was implemented and validated on projects developed in Java, C++ and C#. The validation of the proposed approach was done with expert judgment, where software developers and architects with multiple years of experiences assessed the quality of the software classes. Using a combination of software metrics as the criteria for the identification of deficient source code, the number of potentially deficient object-oriented program entities proved to be reduced. The results show the correctness of quality ratings determined by the proposed identification approach, and most importantly, confirm the absence of false positive entities.

The selection of software metrics for building software quality prediction models is a search‐based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault‐proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real‐world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd.

Improving the design quality of large object‐oriented systems during maintenance and evolution is widely regarded as a high‐priority objective. Furthermore, for such systems that are subject to frequent modifications, detection and correction of design defects may easily become a very complex task that is even not tractable for manual handling. Therefore, the use of automatic or semi‐automatic detection and correction techniques and tools can assist reengineering activities. This paper proposes a framework whereby object‐oriented metrics can be used as indicators for automatically detecting situations for particular transformations to be applied in order to improve specific design quality characteristics. The process is based both on modeling the dependencies between design qualities and source code features, and on analyzing the impact that various transformations have on software metrics that quantify the design qualities being improved. Copyright © 2004 John Wiley & Sons, Ltd.