Program Comprehension During Software Maintenance and Evolution

Computer P rogram understanding is a major factor in providing effective software maintenance and enabling successful evolution of computer systems. For years, researchers have tried to understand how programmers comprehend programs during software maintenance and evolution. Five types of tasks are commonly associated with software maintenance and evolution: adaptive, perfective, and corrective maintenance; reuse; and code leverage. Each task type typically involves certain activities (see Table 1). Some activities, such as understanding the system or problem, are common to several tasks. To analyze the cognitive processes behind these tasks, researchers have developed several models (see Table 2). In this article, we describe common elements of six cognition models and compare them based on their scope and experimental support. All models use existing knowledge to build new knowledge about the mental model of the software that is under consideration. Many of these models are based on exploratory experiments, and some of those models have been validated. Programmers employ various strategies and use cues in code or documentation as guidance. However, their level of expertise greatly influences how efficiently they understand the code. Because of limited knowledge about specialized cognition needs for some maintenance tasks, the code cognition models do not represent every single task listed in Table 1. Most models assume that the objective is to understand all of the code rather than a particular purpose, such as debugging. While these general models can foster a complete understanding of a piece of code, they may not always apply to specialized tasks that more efficiently employ strategies geared toward partial understanding. We identify open questions, particularly considering maintenance and evolution of large-scale code. These questions relate to scalability of existing experimental results with small programs, validity and credibility of results based on experimental procedure, and challenges of data availability.