Software applications have grown increasingly complex to deliver the features desired by users. Software modularity has been used as a way to mitigate the costs of developing such complex software. Active learning-based program inference provides an elegant framework that exploits this modularity to tackle development correctness, performance and cost in large applications. Inferred programs can be used for many purposes, including generation of secure code, code re-use through automatic encapsulation, adaptation to new platforms or languages, and optimization. We show through detailed examples how our approach can infer three modules in a representative application. Finally, we outline the broader paradigm and open research questions.
Wed 23 Oct Times are displayed in time zone: Beirut change
|14:00 - 14:30|
|AlleAlle: Bounded Relational Model Finding with Unbounded Data|
Jouke StoelCWI, Tijs van der StormCWI & University of Groningen, Netherlands, Jurgen VinjuCWI, NetherlandsLink to publication DOI Pre-print
|14:30 - 15:00|
|Active Learning for Software Engineering|