Optimization Techniques for Algorithmic Debugging

Nowadays, undetected programming bugs produce a waste of billions of dollars per year to private and public companies and institutions. In spite of this, no significant advances in the debugging area that help developers along the software development process have been achieved yet. In fact, the same debugging techniques that were used 20 years ago are still being used now. Although some alternatives have appeared, they are still a long way until they become useful enough to be part of the software development process. One of such alternatives is Algorithmic Debugging, which abstracts the information the user has to investigate to debug the program, allowing them to focus on what, rather than how, is happening. This abstraction comes at a price: the granularity level of the bugs that can be detected allows for isolating wrongly implemented functions, but which part of them contains the bug cannot be found out yet. This work is a short introduction of some published papers that focus on improving Algorithmic Debugging in many aspects. Concretely, the main aims of these papers are to reduce the time the user needs to detect a programming bug as well as to provide the user with more detailed information about where the bug is located.