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.
Sun 4 NovDisplayed time zone: Guadalajara, Mexico City, Monterrey change
10:30 - 12:00 | |||
10:30 15mTalk | Total Recall, Language Processing, and Software Engineering NL4SE | ||
10:45 15mTalk | Is “Naturalness” a Result of Deliberate Choice? NL4SE Kevin Lee University of California at Davis, USA, Casey Casalnuovo University of California at Davis, USA | ||
11:00 15mTalk | A Fine-Grained Approach for Automated Conversion of JUnit Assertions to English NL4SE Danielle Gonzalez Rochester Institute of Technology, USA, Suzanne Prentice University of South Carolina, USA, Mehdi Mirakhorli Rochester Institute of Technology | ||
11:15 15mTalk | TestNMT: Function-to-Test Neural Machine Translation NL4SE | ||
11:30 15mTalk | 3CAP: Categorizing the Cognitive Capabilities of Alzheimer’s Patients in a Smart Home Environment NL4SE | ||
11:45 15mTalk | Generating Comments from Source Code with CCGs NL4SE | ||