  A broad class of software engineering problems can be generalized as the
"total recall problem". This short paper claims that identifying and exploring
total recall language processing 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 text mining, solutions of the total recall problem, can
help solve two important software engineering tasks: (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 categorized as 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.
