Moving Fast with Software Verification
By Cristiano Calcagno, Dino Distefano, Jérémy Dubreil, Dominik Gabi, Pieter Hooimeijer, M. Luca, Peter W. O'Hearn, Irene Papakonstantinou, Jim Purbrick, Dulma Rodriguez. NASA Formal Methods, 2015
336 citations recorded by the source corpus.
For organisations like Facebook, high quality software is important. However, the pace of change and increasing complexity of modern code makes it difficult to produce error-free software. Available tools are often lacking in helping programmers develop more reliable and secure applications.
Subfields: Information Systems, Software, Signal Processing
View Paper: https://www.semanticscholar.org/paper/92164d838120de4c4f7423ebebd06524e49dc41e
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Citations in the corpus
- Learning semantic program embeddings with graph interval neural network (2020, methodology): “We also deploy our trained bug detector and Facebook Infer [Berdine et al. 2006; Calcagno et al. 2015], arguably the state-of-the-art static analysis tool, to search bugs in 20 Java projects that are among the most starred on GitHub.”
- Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs (2020, background): “Infer (Calcagno et al., 2015) is built upon sound principles and can prove the absence of certain classes of bugs.”
- Getafix: learning to fix bugs automatically (2019, methodology): “The experiments where conducted on a single machine with 114GB of RAM and Intel Skylake processors with 24 cores running at 2.4GHz. require additional runs of Infer, and besides, may be distracting for the developer.”
- DeepBugs: a learning approach to name-based bug detection (2018, background): “2012], Facebook’s Infer [Calcagno et al. 2015] or FindBugs [Hovemeyer and Pugh 2004].”