This paper reports on a software understanding study during adaptation of large-scale software by software maintenance professionals. The paper explains the general understanding process, the information needs of these software engineers during their tasks, and the tool capabilities that would help them to be more productive. We also contrast programmer behavior during adaptation with that during other maintenance activities like corrective maintenance and enhancement.

We report on a software understanding field study during corrective maintenance of large-scale software by professional software maintenance engineers. We explain the general understanding process, the information needs of these software engineers during their tasks, and the tool capabilities that would help them to be more productive.

This paper reports on an empirical strategy of software understanding during corrective maintenance of large-scale software with professional maintenance programmers. Hypotheses are key drivers in program understanding and influence the direction program understanding can take. This paper reports on the types of hypotheses programmers make, how they resolve them, and the strategies and comprehension processes they tend to use

This paper reports on a software understanding field study during the enhancement of large-scale software. The participants were professional software maintenance personnel from industry. The paper reports on the general understanding process, the kinds of actions programmers preferred during the enhancement task, the level of abstraction at which they were working, and role of hypotheses in the enhancement strategies they used. The results of the observations are also interpreted in terms of the information needs of these personnel during the enhancement task. We found that programmers work predominantly at the code and algorithmic levels with differences depending on the stage of the enhancement. They frequently switch between levels of abstraction. The programmers' main concerns are with what software does and how this is accomplished, not why software was built a certain way. These questions guide the work process. There was strong indication that memory (over)load is an issue. This is, of course, related to the size of the software. Information is sought and cross-referenced from a variety of sources from application domain concepts to code-related information, outpacing current maintenance environments' capabilities which are mostly stratified by information sources, making cross-referencing difficult. © 1997 John Wiley & Sons, Ltd.

We present results of observing professional maintenance engineers working with industrial code at actual maintenance tasks. Protocol analysis is used to explore how code understanding might differ for small versus large scale code. The experiment confirms that cognition processes work at all levels of abstraction simultaneously as programmers build a mental model of the code. Analysis focused on dynamic properties and processes of code understanding. Cognition processes emerged at three levels of aggregation representing lower and higher level strategies of understanding. They show differences in what triggers them and how they achieve their goals. Results are useful for defining information which maintenance engineers need for their work and for documentation and development standards.