An immutable multi-map is a many-to-many map data structure with expected fast insert and lookup operations. This data structure is used for applications processing graphs or many-to-many relations as applied in compilers, runtimes of programming languages, or in static analysis of object-oriented systems. Collection data structures are assumed to carefully balance execution time of operations with memory consumption characteristics and need to scale gracefully from a few elements to multiple gigabytes at least. When processing larger in-memory data sets the overhead of the data structure encoding itself becomes a memory usage bottleneck, dominating the overall performance. In this paper we propose AXIOM, a novel hash-trie data structure that allows for a highly efficient and type-safe multi-map encoding by distinguishing inlined values of singleton sets from nested sets of multi-mappings. AXIOM strictly generalizes over previous hash-trie data structures by supporting the processing of fine-grained type-heterogeneous content on the implementation level (while API and language support for type-heterogeneity are not scope of this paper). We detail the design and optimizations of AXIOM and further compare it against state-of-the-art immutable maps and multi-maps in Java, Scala and Clojure. We isolate key differences using microbenchmarks and validate the resulting conclusions on a case study in static analysis. AXIOM reduces the key-value storage overhead by 1.87 x; with specializing and inlining across collection boundaries it improves by 5.1 x.@en

Context. Software development pipelines are used for automating essential parts of software engineering processes, such as build automation and continuous integration testing. In particular, interactive pipelines, which process events in a live environment such as an IDE, require timely results for low-latency feedback, and persistence to retain low-latency feedback between restarts. Inquiry. Developing an incrementalized and persistent version of a pipeline is one way to reduce feedback latency, but requires implementation of dependency tracking, cache invalidation, and other complicated and error-prone techniques. Therefore, interactivity complicates pipeline development if timeliness and persistence become responsibilities of the pipeline programmer, rather than being supported by the underlying system. Systems for programming incremental and persistent pipelines exist, but do not focus on ease of development, requiring a high degree of boilerplate, increasing development and maintenance effort. Approach. We develop Pipelines for Interactive Environments (PIE), a Domain-Specific Language (DSL), API, and runtime for developing interactive software development pipelines, where ease of development is a focus. The PIE DSL is a statically typed and lexically scoped language. PIE programs are compiled to programs implementing the API, which the PIE runtime executes in an incremental and persistent way. Knowledge. PIE provides a straightforward programming model that enables direct and concise expression of pipelines without boilerplate, reducing the development and maintenance effort of pipelines. Compiled pipeline programs can be embedded into interactive environments such as code editors and IDEs, enabling timely feedback at a low cost. Grounding. Compared to the state of the art, PIE reduces the code required to express an interactive pipeline by a factor of 6 in a case study on syntax-aware editors. Furthermore, we evaluate PIE in two case studies of complex interactive software development scenarios, demonstrating that PIE can handle complex interactive pipelines in a straightforward and concise way. Importance. Interactive pipelines are complicated software artifacts that power many important systems such as continuous feedback cycles in IDEs and code editors, and live language development in language workbenches. New pipelines, and evolution of existing pipelines, is frequently necessary. Therefore, a system for easily developing and maintaining interactive pipelines, such as PIE, is important.@en

Context Context-free grammars are widely used for language prototyping and implementation. They allow formalizing the syntax of domain-specific or general-purpose programming languages concisely and declaratively. However, the natural and concise way of writing a context-free grammar is often ambiguous. Therefore, grammar formalisms support extensions in the form of declarative disambiguation rules to specify operator precedence and associativity, solving ambiguities that are caused by the subset of the grammar that corresponds to expressions.
Inquiry Implementing support for declarative disambiguation within a parser typically comes with one or more of the following limitations in practice: a lack of parsing performance, or a lack of modularity (i.e., disallowing the composition of grammar fragments of potentially different languages). The latter subject is generally addressed by scannerless generalized parsers. We aim to equip scannerless generalized parsers with novel disambiguation methods that are inherently performant, without compromising the concerns of modularity and language composition.
Approach In this paper, we present a novel low-overhead implementation technique for disambiguating deep associativity and priority conflicts in scannerless generalized parsers with lightweight data-dependency.
Knowledge Ambiguities with respect to operator precedence and associativity arise from combining the various operators of a language. While shallow conflicts can be resolved efficiently by one-level tree patterns, deep conflicts require more elaborate techniques, because they can occur arbitrarily nested in a tree. Current state-of-the-art approaches to solving deep priority conflicts come with a severe performance overhead.
Grounding We evaluated our new approach against state-of-the-art declarative disambiguation mechanisms. By parsing a corpus of popular open-source repositories written in Java and OCaml, we found that our approach yields speedups of up to 1.73x over a grammar rewriting technique when parsing programs with deep priority conflicts—with a modest overhead of 1–2 % when parsing programs without deep conflicts.
Importance A recent empirical study shows that deep priority conflicts are indeed wide-spread in real-world programs. The study shows that in a corpus of popular OCaml projects on Github, up to 17 % of the source files contain deep priority conflicts. However, there is no solution in the literature that addresses efficient disambiguation of deep priority conflicts, with support for modular and composable syntax definitions.@en

In layout-sensitive languages, the indentation of an expression or statement can influence how a program is parsed. While some of these languages (e.g., Haskell and Python) have been widely adopted, there is little support for software language engineers in building tools for layout-sensitive languages. As a result, parsers, pretty-printers, program anal-yses, and refactoring tools often need to be handwritten, which decreases the maintainability and extensibility of these tools. Even state-of-the-art language workbenches have little support for layout-sensitive languages, restricting the development and prototyping of such languages. In this paper, we introduce a novel approach to declarative specification of layout-sensitive languages using layout declarations. Layout declarations are high-level specifications of indentation rules that abstract from low-level technicalities. We show how to derive an efficient layout-sensitive generalized parser and a corresponding pretty-printer automatically from a language specification with layout declarations. We validate our approach in a case-study using a syntax definition for the Haskell programming language, investigating the performance of the generated parser and the correctness of the generated pretty-printer against 22191 Haskell files.@en

Context-free grammars are suitable for formalizing the syntax of programming languages concisely and declaratively. Thus, such grammars are often found in reference manuals of programming languages, and used in language workbenches for language prototyping. However, the natural and concise way of writing a context-free grammar is often ambiguous. Safe and complete declarative disambiguation of operator precedence and associativity conflicts guarantees that all ambiguities arising from combining the operators of the language are resolved. Ambiguities can occur due to shallow conflicts, which can be captured by one-level tree patterns, and deep conflicts, which require more elaborate techniques. Approaches to solve deep priority conflicts include grammar transformations, which may result in large unambiguous grammars, or may require adapted parser technologies to include data-dependency tracking at parse time. In this paper we study deep priority conflicts "in the wild". We investigate the efficiency of grammar transformations to solve deep priority conflicts by using a lazy parse table generation technique. On top of lazily-generated parse tables, we define metrics, aiming to answer how often deep priority conflicts occur in real-world programs and to what extent programmers explicitly disambiguate programs themselves. By applying our metrics to a small corpus of popular open-source repositories we found that in OCaml, up to 17% of the source files contain deep priority conflicts.@en