Metric Approach to Anticipate Reusability of Object-Oriented (O-O) Software Systems

In order to meet the fast software evolution, there is a call for the work on software development based process by reducing time as well as efforts. The aim of the development process should not only be developing software products and services but also focus on improving the quality of the particular software. Software Reusability can be considered as one of the solutions to achieve both objectives i.e. productivity as well as quality. There has been an evolution of various methods and techniques related to construction of reusable components over many years. Object-oriented approach also assures increased software reusability. It is easier to reuse object-oriented software rather than conventional software. The notion of reusability related to Object-oriented software can be achieved through inheritance which in turn contributes to development of reusable components. In this paper different metrics related to software reusability of Object-oriented software systems has been summarized and evaluated using Python. Three python-based programs are considered as datasets for this study-the first dataset depicts single-level inheritance, the second dataset depicts hierarchical inheritance whereas the third dataset depicts multilevel inheritance. This study shows more impact of multilevel inheritance on the reusability of Object-oriented software systems and also helped to understand the important role of metrics in evaluation of object-oriented systems.

Hybrid Optimization Driven RideNN for Software Reusability Estimation

Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the Measuring the software reusability has become a prime concern in maintaining the quality of the software. Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges. This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time. Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor. Initially, nine software related metrics are extracted from the software. Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software reusability estimation. The proposed C-RideNN algorithm uses the existing Cat Swarm Optimization (CSO) along with the Rider Neural Network (RideNN) for the training purpose. Experimentation results of the proposed C-RideNN are evaluated based on metrics, such as Magnitude of Absolute Error (MAE), Mean Magnitude of the Relative Error (MMRE), and Standard Error of the Mean (SEM). The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM.

Towards Automatic Updates of Software Dependencies based on Artificial Intelligence

Software reusability encourages developers to heavily rely on a variety of third-party libraries and packages, resulting in dependent software products. Often ignored by developers due to the risk of breakage but dependent software have to adopt security and performance updates in their external dependencies. Existing work advocates a shift towards Automatic updation of dependent software code to implement update dependencies. Emerging automatic dependency management tools notify the availability of new updates, detect their impacts on dependent software and identify potential breakages or other vulnerabilities. However, support for automatic source code refactoring to fix potential breaking changes (to the best of my current knowledge) is missing from these tools. This paper presents a prototyping tool, DepRefactor, that assist in the programmed refactoring of software code caused by automatic updating of their dependencies. To measure the accuracy and effectiveness of DepRefactor, we test it on various students project developed in C#.