JSGLR2 is a java implementation of the Scannerless Generalized LR-parsing (SGLR) algorithm. It employs a modular architecture. This architecture comes with a performance overhead for letting multiple components interact with each other. This paper looks into the size of the performance overhead penalty for the recovery parser variant. It does so by creating an 'inlined' version of the recovery parser variant. The inlined recovery variant is a JSGLR2 implementation that ignores the modular architecture, and hard-codes the components. The performance of the inlined variant is measured with a pre-existing evaluation suite. The results show that there is a performance increase between the original, and the inlined variant.

The Scannerless Generalized-LR (SGLR) parsing algorithm allows parsing of declarative and modular syntax definitions. However, SGLR is notorious for having low performance, negatively impacting its adoption in practice. This paper presents several performance optimizations for JSGLR2, which is an implementation of SGLR. All optimizations are implemented and evaluated in parallel, which is possible due to JSGLR2's modular architecture. The evaluation is performed using existing sources from three different languages. A combined speed-up of 9% up to 44% is achieved, improving the practicality of JSGLR2.

JSGLR2 is a modular Java implementation of the SGLR parsing algorithm that supports systematic benchmarking and improvement of its several parsing variants. By splitting the code into several components, they can be tested in isolation and thus optimized more effortlessly. The modular architecture, although beneficial for efficiently identifying and implementing optimizations, negatively impacts the performance of the parsing algorithm. This paper aims to measure the overhead introduced by the code architecture for one of the variants, more specifically the incremental variant, which combines incremental parsing with SGLR parsing. It does so by comparing the original implementation with a version with the modularity removed. The evaluation is done on programming languages used in practice: Java, WebDSL and SDF3. The results show that the inlined parser outperforms the previous one, achieving speedups of up to 16% in batch parsing and up to 10% in incremental parsing.

The Scannerless Generalized LR (SGLR) parsing algorithm supports the development of composed languages seamlessly but does not support incremental parsing. The Incremental Generalized LR (IGLR) parsing algorithm, on the other hand, does not support the seamless composition of languages. This thesis presents the Incremental Scannerless Generalized LR (ISGLR) parsing algorithm and investigates the effects of combining the SGLR and IGLR parsing algorithms. While the algorithmic differences are orthogonal, the fact that scannerless parsing relies on non-deterministic parsing for disambiguation has a negative impact on incrementality. Nonetheless, we show that the ISGLR parsing algorithm performs better than the batch SGLR parsing algorithm in typical scenarios. On average, the ISGLR parser can reuse 99% of a previous parse result. When parsing from scratch, the ISGLR parser has a 24% run time overhead compared to the SGLR parser, but when parsing incrementally for changes that are smaller than 1% of the input size on average, it has a 9× speedup.