A broad class of software engineering problems can be generalized as the "total recall problem". This short paper claims that identifying and exploring the total recall problems in software engineering is an important task with wide applicability.
To make that case, we show that by applying and adapting the state of the art active learning and natural language processing algorithms for solving the total recall problem, two important software engineering tasks can also be addressed : (a) supporting large literature reviews and (b) identifying software security vulnerabilities. Furthermore, we conjecture that (c) test case prioritization and (d) static warning identification can also be generalized as and benefit from the total recall problem.
The widespread applicability of "total recall" to software engineering suggests that there exists some underlying framework that encompasses not just natural language processing, but a wide range of important software engineering tasks.