The CORE-MATH project aims at providing open-source mathematical functions with correct rounding that can be integrated into current mathematical libraries. This article demonstrates the CORE-MATH methodology on two functions: the binary32 power function (powf) and the binary64 cube root function (cbrt). CORE-MATH already provides a full set of correctly rounded C99 functions for single precision (binary32). These functions provide similar or in some cases up to threefold speedups with respect to the GNU libc mathematic library, which is not correctly rounded. This work offers a prospect of the mandatory requirement of correct rounding for mathematical functions in the next revision of the IEEE-754 standard.

In 2007, the Deckard paper was published at ICSE. Since its publication, it has led to much follow-up research and applications. The paper made two core contributions: a novel vector embedding of structured code for fast similarity detection, and an application of the embedding for clone detection, resulting in the Deckard tool. The vector embedding is simple and easy to adapt. Similar code detection is also fundamental for a range of classical and emerging problems in software engineering, security, and computer science education (e.g., code reuse, refactoring, porting, translation, synthesis, program repair, malware detection, and feedback generation). Both have buttressed the paper’s influence.

Detecting code clones has many software engineering applications. Existing approaches either do not scale to large code bases or are not robust against minor code modifications. In this paper, we present an efficient algorithm for identifying similar subtrees and apply it to tree representations of source code. Our algorithm is based on a novel characterization of subtrees with numerical vectors in the Euclidean space Rnmiddot and an efficient algorithm to cluster these vectors w.r.t. the Euclidean distance metric. Subtrees with vectors in one cluster are considered similar. We have implemented our tree similarity algorithm as a clone detection tool called DECKARD and evaluated it on large code bases written in C and Java including the Linux kernel and JDK. Our experiments show that DECKARD is both scalable and accurate. It is also language independent, applicable to any language with a formally specified grammar.