Language Independent Static Analysis (LISA)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Software is becoming increasingly important in nearly every aspect of global society and therefore in nearly every aspect of national security as well. While there have been major advancements in recent years in formally proving properties of program source code during development, such approaches are still in the minority among development teams, and the vast majority of code in this software explosion is produced without such properties. In these cases, the source code must be analyzed in order to establish whether the properties of interest hold. Because of the volume of software being produced, automated approaches to software analysis are necessary to meet the need. However, this software boom is not occurring in just one language. There are a wide range of languages of interest in national security spaces, including well-known languages such as C, C++, Python, Java, Javascript, and many more. But recent years have produced a wide range of new languages, including Nim, (2008), Go (2009), Rust (2010), Dart (2011), Kotlin (2011), Elixir (2011), Red (2011), Julia (2012), Typescript (2012), Swift (2014), Hack (2014), Crystal (2014), Ballerina (2017) and more. Historically, automated software analyses are implemented as tools that intermingle both the analysis question at hand with target language dependencies throughout their code, making re-use of components for different analysis questions or different target languages impractical. This project seeks to explore how mission-relevant, static software analyses can be designed and constructed in a language-independent fashion, dramatically increasing the reusability of software analysis investments.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1673452
- Report Number(s):
- SAND--2020-10486; 691339
- Country of Publication:
- United States
- Language:
- English
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