SOFTWARE QUALITY: DUAL EXPERTS OPINION AND CONDITIONAL BASED AGGREGATION METHOD

The software reliability is the significant factor to find out software failures in software development Life Cycle. The one more factor considered is the quality of software measurement process. These two factors are mostly considered for the possibility of execution of the software without failures in a software development life cycle. The software reliability and software quality cannot be predicted accurately because of its unsuccessful detection of failures in certain scenarios. This paper mainly focuses on improving the software engineering metrics using an expert opinion and in order to resolve the software failures. On choosing the software engineering measures there are different types of problem that are been occurred in that in this paper we have taken two main issues. The first issue is number of measures that are utilized in estimating software quality and these software measures are chosen with the help of expert opinion. However, the experts are humans so they may have less adequate knowledge about different software evaluations. The Problem is resolved by taking consideration with first level and second level of experts ’ opinion for selecting the best measures for software quality. The second issue is of data aggregation function which is not suitable for large number of data aggregations, here in this paper we select a prioritized opinion for data aggregation. The prioritization is based on number of experts involved in each life-cycle phase of software development with time duration to give the opinion. Finally the experiments results are shown for the software quality improvisation by the proposed framework

A User-aware Intelligent Refactoring for Discrete and Continuous Software Integration

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. Furthermore, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. In addition, it is challenging to merge these refactorings with other changes performed in parallel by developers. In this thesis, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses Non-dominated Sorting Genetic Algorithm (NSGAII) to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. We combined our interactive approach and unsupervised learning to reduce the developer’s interaction effort when refactoring a system. The unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. To reduce the interaction effort, we propose an approach to convert multi-objective search into a mono-objective one after interacting with the developer to identify a good refactoring solution based on their preferences. Since developers may want to focus on specific code locations, the ”Decision Space” is also important. Therefore, our interactive approach enables developers to pinpoint their preference simultaneously in the objective (quality metrics) and decision (code location) spaces. Due to an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Our bot continuously monitors the software repository and find the best sequence of refactorings to fix the quality issues in Continous Integration/Continous Development (CI/CD) environments as a set of pull-requests generated after mining previous code changes to understand the profile of developers. We quantitatively and qualitatively evaluated the performance and effectiveness of our proposed approaches via a set of studies conducted with experienced developers who used our tools on both open source and industry projects.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154775/1/Vahid Alizadeh Final Dissertation.pdfDescription of Vahid Alizadeh Final Dissertation.pdf : Dissertatio

Process Based Unification for Multi-Model Software Process Improvement

A number of differences among quality approaches exist and there can be various situations in which the usage of multiple approaches is required, e.g. to strengthen a particular process with multiple quality approaches or to reach certification of the compliance to a number of standards. First of all it has to be decided which approaches have potential for the organization. In many cases one approach does not contain enough information for process implementation. Consequently, the organization may need to use several approaches and the decision has to be made how the chosen approaches can be used simultaneously. This area is called Multi-model Software Process Improvement (MSPI). The simultaneous usage of multiple quality approaches is called the multi-model problem. In this dissertation we propose a solution for the multi-model problem which we call the Process Based Unification (PBU) framework. The PBU framework consists of the PBU concept, a PBU process and the PBU result. We call PBU concept the mapping of quality approaches to a unified process. The PBU concept is operationalized by a PBU process. The PBU result includes the resulting unified process and the mapping of quality approaches to the unified process.Comment: PhD Thesi