Software products in the market are changing due to changes in business processes, technology, or new requirements from the customers. Maintainability of legacy systems has always been an inspiring task for the software companies. In order to determine whether the software requires maintainability by reverse engineering or by forward engineering approach, a system assessment was done from diverse perspectives: quality, business value, type of errors, etc. In this research, the changes required in the existing software components of the legacy system were identified using a supervised learning approach. New interfaces for the software components were redesigned according to the new requirements and/or type of errors. Software maintainability was measured by applying a machine learning technique, i.e., Naïve Bayes classifier. The dataset was designed based on the observations such as component state, successful or error type in the component, line of code of error that exists in the component, component business value, and changes required for the component or not. The results generated by the Waikato Environment for Knowledge Analysis (WEKA) software confirm the effectiveness of the introduced methodology with an accuracy of 97.18%.

Due to advancements in science and technology, software is constantly evolving. To adapt to newly demanded requirements in a piece of software, software components are modified or developed. Measuring software completeness has been a challenging task for software companies. The uncertain and imprecise intrinsic relationships within software components have been unaddressed by researchers during the validation process. In this study, we introduced a new fuzzy logic testing approach for measuring the completeness of software. We measured the fuzzy membership value for each software component by a fuzzy logic testing approach called the fuzzy test. For each software component, the system response was tested by identifying which software components in the system required changes. Based on the measured fuzzy membership values for each software component, software completeness was calculated. The introduced approach scales the software completeness between zero and one. A software component with a complete membership value indicates that the software component does not require any modification. A non-membership value specifies that the existing software component is no longer required in the system or that a new software component is required to replace it. The partial membership value specifies that the software component requires few new functionalities according to the new software requirements. Software with a partial membership value requires partial restructuring and design recovery of its components. Symmetric design of software components reduces the complexity in the restructuring of software during modification. In the study, we showed that by using the introduced approach, high-quality software that is faultless, reliable, easily maintained, efficient, and cost-effective can be developed.

A new approach, to measure normalization completeness for conceptual model, is introduced using quantitative fuzzy functionality in this paper. We measure the normalization completeness of the conceptual model in two steps. In the first step, different normalization techniques are analyzed up to Boyce Codd Normal Form (BCNF) to find the current normal form of the relation. In the second step, fuzzy membership values are used to scale the normal form between 0 and 1. Case studies to explain schema transformation rules and measurements. Normalization completeness is measured by considering completeness attributes, preventing attributes of the functional dependencies and total number of attributes such as if the functional dependency is non-preventing then the attributes of that functional dependency are completeness attributes. The attributes of functional dependency which prevent to go to the next normal form are called preventing attributes.Comment: 8 Page

A new approach, to measure normalization completeness for conceptual model, is introduced using quantitative fuzzy functionality in this paper. We measure the normalization completeness of the conceptual model in two steps. In the first step, different normalization techniques are analyzed up to Boyce Codd Normal Form (BCNF) to find the current normal form of the relation. In the second step, fuzzy membership values are used to scale the normal form between 0 and 1. Case studies to explain schema transformation rules and measurements. Normalization completeness is measured by considering completeness attributes, preventing attributes of the functional dependencies and total number of attributes such as if the functional dependency is non-preventing then the attributes of that functional dependency are completeness attributes. The attributes of functional dependency which prevent to go to the next normal form are called preventing attributes.

Traditional methods for designing metasurfaces with the intended scattering response are frequently laborious and time-consuming. Selecting the structure’s shape, substrate type, and structure dimensions requires a great deal of design expertise. This paper proposes a time and resource-efficient dual-task multichannel machine learning (DTM2L) framework for the constrained inverse design of multiple metasurface structures. The proposed DTM2L framework is used for joint classification and regression for the prediction of the type and dimensions of the structure. Several examples to achieve a desired scattering response with the constraint on the frequency are provided to verify the performance of the proposed method.