Software Maintenance Prediction: An Architecture Perspective

Abstract

Every successful software product is required to be maintained at some later stage of its life cycle. This maintenance is constrained in different ways by its cost, lead-time, and quality. These constraints are not always straightforward enough to be precisely quantified and understood. If a company can predict the maintenance in advance it can plan its business accordingly. This can be attributed to software engineer’s ability to model the predicted maintenance effort required early in the development cycle. The customers now have keen interest in software quality requirements (i.e. quality attributes (QAs)), like maintainability, reliability, performance, and flexibility too. Therefore, the maintenance may be required from the product’s functionality or its quality point of view. Software architecture is an excellent vehicle to showcase the changes, especially related to QAs that take place over the time. The QAs are interlinked with each other that make the maintenance effort estimation more complex. Unless, we have effective maintenance profiles maintained from very early in the development cycle, it is quite difficult to achieve quality according to customer satisfaction. In this thesis, efforts have been made to identify mechanisms which ensure early maintenance prediction. Software architecture, in our opinion, is one such artifact which can effectively be used to address the above mentioned problems. Software architecture has assumed greater significance over the past two decades as an artifact which not only plays vital role in development of quality software but also in its maintenance and up gradation. In this thesis a methodology for prediction of software system’s maintenance based upon their software architecture design has been defined. As a secondary step the thesis also presents a complete methodology for ripple effect analysis based on quality attribute in order to cover the maintenance of software quality in addition to its functionality. The proposed method enables us to predict the overall maintenance effort quite early in the software development life cycle and provides an insight whether the components will remain consistent after the change or it will be come fluent and inconsistent. The proposed method can also help in better understandability of the software and consequently more reliable early planning.