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

A novel model for improving the maintainability of web-based systems

Web applications incorporate important business assets and offer a convenient way for businesses to promote their services through the internet. Many of these web applica- tions have evolved from simple HTML pages to complex applications that have a high maintenance cost. This is due to the inherent characteristics of web applications, to the fast internet evolution and to the pressing market which imposes short development cycles and frequent modifications. In order to control the maintenance cost, quantita- tive metrics and models for predicting web applications’ maintainability must be used. Maintainability metrics and models can be useful for predicting maintenance cost, risky components and can help in assessing and choosing between different software artifacts. Since, web applications are different from traditional software systems, models and met- rics for traditional systems can not be applied with confidence to web applications. Web applications have special features such as hypertext structure, dynamic code generation and heterogenousity that can not be captured by traditional and object-oriented metrics. This research explores empirically the relationships between new UML design met- rics based on Conallen’s extension for web applications and maintainability. UML web design metrics are used to gauge whether the maintainability of a system can be im- proved by comparing and correlating the results with different measures of maintain- ability. We studied the relationship between our UML metrics and the following main- tainability measures: Understandability Time (the time spent on understanding the soft- ware artifact in order to complete the questionnaire), Modifiability Time(the time spent on identifying places for modification and making those modifications on the software artifact), LOC (absolute net value of the total number of lines added and deleted for com- ponents in a class diagram), and nRev (total number of revisions for components in a class diagram). Our results gave an indication that there is a possibility for a relationship to exist between our metrics and modifiability time. However, the results did not show statistical significance on the effect of the metrics on understandability time. Our results showed that there is a relationship between our metrics and LOC(Lines of Code). We found that the following metrics NAssoc, NClientScriptsComp, NServerScriptsComp, and CoupEntropy explained the effort measured by LOC(Lines of Code). We found that NC, and CoupEntropy metrics explained the effort measured by nRev(Number of Revi- sions). Our results give a first indication of the usefulness of the UML design metrics, they show that there is a reasonable chance that useful prediction models can be built from early UML design metrics

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

WapMetrics: a tool for computing UML design metrics for Web applications

Many companies are still asking how to assess and predict the maintenance cost of their software. Measures of software maintenance cost can be taken either late or early in the development process. Early measures of software maintenance cost are beneficial because they can help in allocating project resources efficiently, predicting the effort of maintenance tasks and controlling the maintenance process. This paper describes a tool for computing early metrics from UML class diagrams based on the Web Application Extension (WAE) for UML. A case study is used to show the usefulness and effectiveness of the tool

Design metrics for web application maintainability measurement

Many web applications have evolved from simple HTML pages to complex applications that have a high maintenance cost. This high maintenance cost is due to the heterogeneity of web applications, to fast Internet evolution and the fast- moving market which imposes short development cycles and frequent modifications. In order to control the maintenance cost, quantitative metrics for predicting web applications maintainability must be used. This paper provides an exploratory study for new design metrics used for measuring the maintainability of web applications from class diagrams. The metrics are based on Web Application Extension (WAE)for UML and will measure the following design attributes: size, complexity, coupling and reusability. In this study the metrics are applied to two web applications from the telecommunications domain

Model-based risk assessment

In this research effort, we focus on model-based risk assessment. Risk assessment is essential in any plan intended to manage software development or maintenance process. Subjective techniques are human intensive and error-prone. Risk assessment should be based on architectural attributes that we can quantitatively measure using architectural level metrics. Software architectures are emerging as an important concept in the study and practice of software engineering nowadays, due to their emphasis on large-scale composition of software product, and to their support for emerging software engineering paradigms, such as product line engineering, component based software engineering, and software evolution.;In this dissertation, we generalize our earlier work on reliability-based risk assessment. We introduce error propagation probability in the assessment methodology to account for the dependency among the system components. Also, we generalize the reliability-based risk assessment to account for inherent functional dependencies.;Furthermore, we develop a generic framework for maintainability-based risk assessment which can accommodate different types of software maintenance. First, we introduce and define maintainability-based risk assessment for software architecture. Within our assessment framework, we investigate the maintainability-based risk for the components of the system, and the effect of performing the maintenance tasks on these components. We propose a methodology for estimating the maintainability-based risk when considering different types of maintenance. As a proof of concept, we apply the proposed methodology on several case studies. Moreover, we automate the estimation of the maintainability-based risk assessment methodology

Proactive Quality Guidance for Model Evolution in Model Libraries

Model evolution in model libraries differs from general model evolution. It limits the scope to the manageable and allows to develop clear concepts, approaches, solutions, and methodologies. Looking at model quality in evolving model libraries, we focus on quality concerns related to reusability. In this paper, we put forward our proactive quality guidance approach for model evolution in model libraries. It uses an editing-time assessment linked to a lightweight quality model, corresponding metrics, and simplified reviews. All of which help to guide model evolution by means of quality gates fostering model reusability.Comment: 10 pages, figures. Appears in Models and Evolution Workshop Proceedings of the ACM/IEEE 16th International Conference on Model Driven Engineering Languages and Systems, Miami, Florida (USA), September 30, 201