Software application metric for aging

Introduction: 

Software development life cycle is often used to indicate the ritual that involves planning, creating, testing and deploying an information system. As the cycle repeats there is a lot of time involved maintenance. As the software ages, it spends more time in this stage. This essay tries to articulate a metric for determining the age of software and how to keep it fresh. 

Description: 

Software quality metrics, generally, fall in three categories. They are either: 

Product based – These capture the characteristics of the product such as size, complexity, design features, performance and quality level. 

Process based – These attempt to track the activities taken for development and maintenance of software. 

Project based – These attempt to describe the resources, timeline, cost, schedule and productivity associated with the software project.  

The metrics associated with software quality are more typical to be related to product based and process based rather than project based.  Yet they do not indicate whether the software has matured – a term loosely used to describe higher costs for fixing defects such that the return on investment for maintenance activities is well over the cost of newly written software.  

Periodic refactoring and rewriting modules and components of the software somehow alleviates massive rewrites by replacing smaller chunks of the overall code. Most applications are well organized and written from the start as allowing flexibility with little or controlled changes to the code. Yet a single method might become overwhelmingly complex over time with the addition of more and more branching of logic. For example a method to draw a shape might require different handling depending on the parameters passed to it. Consequently the code becomes so convoluted with handling these different cases that it is termed spaghetti code. A metric for nested branching is called cyclomatic complexity and it indicates how deep the branching goes before reaching a result. In some sense metrics such as cyclomatic complexity determine the current state of the software but there is no metric that keeps track of the progress of these complexity over time  

In this regard, a metric known as entropy is used to indicate the level of maintenance involved in any module or organizational unit of software. Such a metric that can progress monotonically over time and continues to remain stable and beyond compromise, then becomes a great indicator for aging. With the help of vectors and features, software organization units can now be represented and classified with the same rigor as vector model space. Therefore a multidimensional metric involving multiple scalars then becomes convenient to indicate the age of the software.  

Conclusion:  

Software metric for aging continues to be a challenge but advances in using vector space model along with neural net can help determine the pain points better allowing the overall software to remain young.