Code Summarization for Opportunistic Reuse in Large Software Systems

Abstract

Developers perform a wide variety of tasks during software development and maintenance. Each task varies in complexity and effort. They find comprehension of collected information laborious and often repetitive. Complete comprehension is difficult as software systems grow in size and get more distributed. Due to this, developers have a fragmented or incomplete understanding of the software system. Developers choose to read software code despite the availability of modern tools and Integrated Development Environment (IDE). But, the effectiveness of comprehension by reading code is unknown. So, supporting developers to acquire task-specific knowledge rapidly while they read code is essential to develop and maintain a software system effectively. In this context, we analyze the developers information needs, propose a model and developed a technique to help faster code comprehension. newline newline There is a growing interest in understanding developers information needs when reading code. Since, a comprehension task beings with an information-seeking activity we base our approach on the program navigation models where cues are used to establish the relevance of a piece of information with a task. We explore how the cues influence a developer s perception, their cue choices and IDE support for cue-based knowledge acquisition. We found cue choices were task-dependent varied across different categoires of developers and that IDE has no significant influence on cue choices of developers. To support developers we generated program summaries using a task-specific cue model. We generated contextual summaries using the task-specific cue model. Our synthesized summaries that were 10% of lines of code comprehended amd were 80% similar to the developer recorded summaries. These results suggest that the cue model can significantly improve the quality of program summaries and can aid developers in reading code faster. We further extend the cue model to help developers perform impact analysis faster and improve code readability.