ACM SIGPLAN Notices
ISSN / EISSN : 0362-1340 / 1558-1160
Published by: Institute of Electrical and Electronics Engineers (IEEE) (10.1145)
Total articles ≅ 9,681
Latest articles in this journal
ACM SIGPLAN Notices, Volume 53, pp 14-27; https://doi.org/10.1145/3393934.3278139
Program generation is indispensable. We propose a novel unification of two existing metaprogramming techniques: multi-stage programming and hygienic generative macros. The former supports runtime code generation and execution in a type-safe manner while the latter offers compile-time code generation. In this work we draw upon a long line of research on metaprogramming, starting with Lisp, MetaML and MetaOCaml. We provide direct support for quotes, splices and top-level splices, all regulated uniformly by a level-counting Phase Consistency Principle. Our design enables the construction and combination of code values for both expressions and types. Moreover, code generation can happen either at runtime à la MetaML or at compile time, in a macro fashion, à la MacroML. We provide an implementation of our design in Scala and we present two case studies. The first implements the Hidden Markov Model, Shonan Challenge for HPC. The second implements the staged streaming library Strymonas.
ACM SIGPLAN Notices, Volume 53, pp 53-65; https://doi.org/10.1145/3393934.3278128
Ownership type qualifiers clarify aliasing invariants that cannot be directly expressed in mainstream programming languages. Adding qualifiers to code, however, often involves significant overhead and difficult interaction. We propose an analysis to infer qualifiers in the code based on developer refinements that express strict encapsulation, logical containment and architectural tiers. Refinements include: makeOwnedBy, to make an object strictly encapsulated by another; makePartOf, to make an object logically contained in another; makePeer, to make two objects peers; makeParam, to make an object more accessible than the above choices; or makeShared, to allow an object to be globally aliased. If the code as-written matches the requested refinements, the analysis generates qualifiers that type-check; otherwise, it reports that the refinements do not match the code, so developers must investigate unexpected aliasing, change their understanding of the code and pick different refinements, or change the code and re-run the analysis. We implement the analysis and confirm that refinements generate precise qualifiers that express strict encapsulation, logical containment and architectural tiers.
ACM SIGPLAN Notices, Volume 53, pp 175-187; https://doi.org/10.1145/3393934.3278137
Real-time systems must meet strict timeliness requirements. These systems also often need to protect their critical program information (CPI) from adversarial interference and intellectual property theft. Trusted execution environments (TEE) execute CPI tasks on a special-purpose processor, thus providing hardware protection. However, adapting a system written to execute in environments without TEE requires partitioning the code into the regular and trusted parts. This process involves complex manual program transformations that are not only laborious and intellectually tiresome, but also hard to validate and verify for the adherence to real-time constraints. To address these problems, this paper presents novel program analyses and transformation techniques, accessible to the developer via a declarative meta-programming model. The developer declaratively specifies the CPI portion of the system. A custom static analysis checks CPI specifications for validity, while probe-based profiling helps identify whether the transformed system would continue to meet the original real-time constraints, with a feedback loop suggesting how to modify the code, so its CPI can be isolated. Finally, an automated refactoring isolates the CPI portion for TEE-based execution, communicated with through generated calls to the TEE API. We have evaluated our approach by successfully enabling the trusted execution of the CPI portions of several microbenchmarks and a drone autopilot. Our approach shows the promise of declarative meta-programming in reducing the programmer effort required to adapt systems for trusted execution under real-time constraints.
ACM SIGPLAN Notices, Volume 53, pp 28-39; https://doi.org/10.1145/3393934.3278129
Command languages like the Bourne Shell provide a terse syntax for exploratory programming and system interaction. Shell users can begin to write programs that automate their tasks by simply copying their interactions verbatim into a script file. However, command languages usually scale poorly beyond small scripts, and they can be difficult to integrate into larger programs. General-purpose languages scale well, but are verbose and unwieldy for common interactive actions such as process composition. We present Rash, a domain-specific command language embedded in Racket. Rash provides a terse and extensible syntax for interactive system administration and scripting, as well as easy composition of both Racket functions and operating system processes. Rash and normal Racket code can be nested together at the expression level, providing the benefits of a shell language and a general-purpose language together. Thus, Rash provides a gradual scale between shell-style interactions and general-purpose programming.
ACM SIGPLAN Notices, Volume 53, pp 1-13; https://doi.org/10.1145/3393934.3278138
With an ever-growing amount of collected data, the importance of visualization as an analysis component is growing in concert. The creation of good visualizations often doesn't happen in one step but is rather an iterative and exploratory process. However, this process is currently not well supported in most of the available visualization tools and systems. Visualization authors are forced to commit prematurely to particular design aspects of their creations, and when exploring potential variant visualizations, they are forced to adopt ad hoc techniques such as copying code snippets or keeping a collection of separate files. We propose variational visualizations as a model supporting open-ended exploration of the design space of information visualization. Together with that model, we present a prototype implementation in the form of a domain-specific language embedded in Purescript.
ACM SIGPLAN Notices, Volume 53, pp 79-92; https://doi.org/10.1145/3393934.3278131
Many modern application domains crucially rely on tensor operations. The optimization of programs that operate on tensors poses difficulties that are not adequately addressed by existing languages and tools. Frameworks such as TensorFlow offer good abstractions for tensor operations, but target a specific domain, i.e. machine learning, and their optimization strategies cannot easily be adjusted to other domains. General-purpose optimization tools such as Pluto and existing meta-languages offer more flexibility in applying optimizations but lack abstractions for tensors. This work closes the gap between domain-specific tensor languages and general-purpose optimization tools by proposing the Tensor optimizations Meta-Language (TeML). TeML offers high-level abstractions for both tensor operations and loop transformations, and enables flexible composition of transformations into effective optimization paths. This compositionality is built into TeML's design, as our formal language specification will reveal. We also show that TeML can express tensor computations as comfortably as TensorFlow and that it can reproduce Pluto's optimization paths. Thus, optimized programs generated by TeML execute at least as fast as the corresponding Pluto programs. In addition, TeML enables optimization paths that often allow outperforming Pluto.
ACM SIGPLAN Notices, Volume 53, pp 147-160; https://doi.org/10.1145/3393934.3278125
High-level transformation languages like Rascal include expressive features for manipulating large abstract syntax trees: first-class traversals, expressive pattern matching, backtracking and generalized iterators. We present the design and implementation of an abstract interpretation tool, Rabit, for verifying inductive type and shape properties for transformations written in such languages. We describe how to perform abstract interpretation based on operational semantics, specifically focusing on the challenges arising when analyzing the expressive traversals and pattern matching. Finally, we evaluate Rabit on a series of transformations (normalization, desugaring, refactoring, code generators, type inference, etc.) showing that we can effectively verify stated properties.
ACM SIGPLAN Notices, Volume 53, pp 66-78; https://doi.org/10.1145/3393934.3278134
In spite of the fact that many sensors in use today are binary (i.e. produce only values of 0 and 1), and that useful context-aware applications are built exclusively on top of them, there is currently no development approach specifically targeted to binary sensors. Dealing with notions of state and state combinators, central to binary sensors, is tedious and error-prone in current approaches. For instance, developing such applications in a general programming language requires writing code to process events, maintain state and perform state transitions on events, manage timers and/or event histories. In another paper, we introduced a domain specific language (DSL) called Allen, specifically targeted to binary sensors. Allen natively expresses states and state combinations, and detects contexts on line, on incoming streams of binary events. Expressing state combinations in Allen is natural and intuitive due to a key ingredient: semi-causal operators. That paper focused on the concept of the language and its main operators, but did not address its implementation challenges. Indeed, online evaluation of expressions containing semi-causal operators is difficult, because semi-causal sub-expressions may block waiting for future events, thus generating unknown values, besides 0 and 1. These unknown values may or may not propagate to the containing expressions, depending on the current value of the other arguments. This paper presents a compiler and runtime for the Allen language, and shows how they implement its state combining operators, based on reducing complex expressions to a core subset of operators, which are implemented natively. We define several assisted living applications both in Allen and in a general scripting language. We show that the former are much more concise in Allen, achieve more effective code reuse, and ease the checking of some domain properties.
ACM SIGPLAN Notices, Volume 53, pp 134-146; https://doi.org/10.1145/3393934.3278124
Pattern matching is a pervasive and useful feature in functional programming. There have been many attempts to bring similar notions to Object-Oriented Programming (OOP) in the past. However, a key challenge in OOP is how pattern matching can coexist with the open nature of OOP data structures, while at the same time guaranteeing other desirable properties for pattern matching. This paper discusses several desirable properties for pattern matching in an OOP context and shows how existing approaches are lacking some of these properties. We argue that the traditional semantics of pattern matching, which is based on the order of patterns and adopted by many approaches, is in conflict with the openness of data structures. Therefore we suggest that a more restricted, top-level pattern matching model, where the order of patterns is irrelevant, is worthwhile considering in an OOP context. To compensate for the absence of ordered patterns we propose a complementary mechanism for case analysis with defaults, which can be used when nested and/or multiple case analysis is needed. To illustrate our points we develop Castor: a meta-programming library inScala that adopts both ideas. Castor generates code that uses type-safe extensible visitors, and largely removes boilerplate code typically associated with visitors. We illustrate the applicability of our approach with a case study modularizing the interpreters in the famous book ”Types and Programming Languages”.
ACM SIGPLAN Notices, Volume 53, pp 40-52; https://doi.org/10.1145/3393934.3278127
In highly configurable systems, features may interact unexpectedly and produce faulty behavior. Those faults are not easily identified from the analysis of each feature separately, especially when feature specifications are missing. We propose VarXplorer, a dynamic and iterative approach to detect suspicious interactions. It provides information on how features impact the control and data flow of the program. VarXplorer supports developers with a graph that visualizes this information, mainly showing suppress and require relations between features. To evaluate whether VarXplorer helps improve the performance of identifying suspicious interactions, we perform a controlled study with 24 subjects. We find that with our proposed feature-interaction graphs, participants are able to identify suspicious interactions more than 3 times faster compared to the state-of-the-art tool.