Learning from experience in a software maintenance environment

This study reports results from an empirical study of 109 software maintenance tasks in the software maintenance department of a Norwegian company. Findings were, amongst others, that: • While there was a reduction in the frequency of major unexpected problems from tasks solved by very inexperienced to medium experienced maintainers, additional years of experience did not lead to further reduction. • Increased experience did not lead to higher accuracy when predicting maintenance problems. • The prediction performance seems to be poor. A simple one-variable model outperformed the maintainer’s predictions of maintenance problems. Our findings are supported by other studies reporting similar poor learning from experience in other domains and can be explained by learning hindrances in the maintenance process. To improve the learning from experience we recommend a stronger focus on creating better learning situations, higher quality of feedback, and training in the principles of probability.