Total Recall, Language Processing, and Software Engineering

TL;DR

This paper introduces the 'total recall' problem as a unifying framework for various software engineering tasks, demonstrating how active learning and text mining can address issues like literature review and security vulnerability detection.

Contribution

It proposes the total recall problem as a broad, unifying framework for software engineering tasks and shows how existing NLP techniques can be adapted to solve them.

Findings

Active learning and text mining can effectively address total recall problems.

Total recall framework applies to literature reviews and security vulnerability detection.

Potential extension to test case prioritization and static warning identification.

Abstract

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.