Total Recall, Language Processing, and Software Engineering

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.Comment: 4 pages, 2 figures. Submitted to NL4SE@ESEC/FSE 201

Semi-Automated Analysis of Large Privacy Policy Corpora

Regulators, policy makers, and consumers are interested in proactively identifying services with acceptable or compliant data use policies, privacy policies, and terms of service. Academic requirements engineering researchers and legal scholars have developed qualitative, manual approaches to conducting requirements analysis of policy documents to identify concerns and compare services against preferences or standards. In this research, we develop and present an approach to conducting large-scale, qualitative, prospective analyses of policy documents with respect to the wide-variety of normative concerns found in policy documents. Our approach uses techniques from natural language processing, including topic modeling and summarization. We evaluate our approach in an exploratory case study that attempts to replicate a manual legal analysis of roughly 200 privacy policies from seven domains in a semi-automated fashion at a larger scale. Our findings suggest that this approach is promising for some concerns