Total Recall, Language Processing, and Software Engineering
This work addresses software engineering challenges by proposing a unifying framework, but it appears incremental as it adapts existing methods to new domains without major breakthroughs.
The paper tackles the generalization of software engineering problems as the 'total recall problem' and demonstrates that applying state-of-the-art active learning and text mining can solve tasks like large literature reviews and identifying security vulnerabilities, with potential applications to test case prioritization and static warning identification.
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.