ACM SIGPLAN Notice

Smeltzer, Karl; Erwig, Martin

doi: 10.1145/3393934.3278138pmid: N/A

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.

Stucki, Nicolas; Biboudis, Aggelos; Odersky, Martin

doi: 10.1145/3393934.3278139pmid: N/A

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.

Hatch, William Gallard; Flatt, Matthew

doi: 10.1145/3393934.3278129pmid: N/A

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.

Soares, Larissa Rocha; Meinicke, Jens; Nadi, Sarah; Kästner, Christian; de Almeida, Eduardo Santana

doi: 10.1145/3393934.3278127pmid: N/A

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.

Khalaj, Ebrahim; Abi-Antoun, Marwan

doi: 10.1145/3393934.3278128pmid: N/A

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: <pre>makeOwnedBy</pre>, to make an object strictly encapsulated by another; <pre>makePartOf</pre>, to make an object logically contained in another; <pre>makePeer</pre>, to make two objects peers; <pre>makeParam</pre>, to make an object more accessible than the above choices; or <pre>makeShared</pre>, 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.

Volanschi, Nic; Serpette, Bernard; Consel, Charles

doi: 10.1145/3393934.3278134pmid: N/A

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.

Susungi, Adilla; Rink, Norman A.; Cohen, Albert; Castrillon, Jeronimo; Tadonki, Claude

doi: 10.1145/3393934.3278131pmid: N/A

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.

Peldszus, Sven; Strüber, Daniel; Jürjens, Jan

doi: 10.1145/3393934.3278126pmid: N/A

Today's software systems are too complex to ensure security after the fact – security has to be built into systems by design. To this end, model-based techniques such as UMLsec support the design-time specification and analysis of security requirements by providing custom model annotations and checks. Yet, a particularly challenging type of complexity arises from the variability of software product lines. Analyzing the security of all products separately is generally infeasible. In this work, we propose SecPL, a methodology for ensuring security in a software product line. SecPL allows developers to annotate the system design model with product-line variability and security requirements. To keep the exponentially large configuration space tractable during security checks, SecPL provides a family-based security analysis. In our experiments, this analysis outperforms the naive strategy of checking all products individually. Finally, we present the results of a user study that indicates the usability of our overall methodology.

Christophe, Laurent; De Roover, Coen; Boix, Elisa Gonzalez; De Meuter, Wolfgang

doi: 10.1145/3393934.3278135pmid: N/A

Dynamic analyses are commonly implemented by instrumenting the program under analysis. Examples of such analyses for JavaScript range from checkers of user- defined invariants to concolic testers. For a full-stack JavaScript program, these analyses would benefit from reasoning about the state of the client-side and server-side processes it is comprised of. Lifting a dynamic analysis so that it supports full-stack programs can be challenging. It involves distributed communication to maintain the analysis state across all processes, which has to be deadlock-free. In this paper, we advocate maintaining distributed analysis state in a centralized analysis process instead — which is communicated with from the processes under analysis. The approach is supported by a dynamic analysis platform that provides abstractions for this communication. We evaluate the approach through a case study. We use the platform to build a distributed origin analysis, capable of tracking the expressions from which values originate from across process boundaries, and deploy it on collaborative drawing application. The results show that our approach greatly simplifies the lifting process at the cost of a computational overhead. We deem this overhead acceptable for analyses intended for use at development time.

Ruland, Sebastian; Luthmann, Lars; Bürdek, Johannes; Lity, Sascha; Thüm, Thomas; Lochau, Malte; Ribeiro, Márcio

doi: 10.1145/3393934.3278130pmid: N/A

Recent research on quality assurance (QA) of configurable software systems (e.g., software product lines) proposes different analysis strategies to cope with the inherent complexity caused by the well-known combinatorial-explosion problem. Those strategies aim at improving efficiency of QA techniques like software testing as compared to brute-force configuration-by-configuration analysis. Sampling constitutes one of the most established strategies, defining criteria for selecting a drastically reduced, yet sufficiently diverse subset of software configurations considered during QA. However, finding generally accepted measures for assessing the impact of sample-based analysis on the effectiveness of QA techniques is still an open issue. We address this problem by lifting concepts from single-software mutation testing to configurable software. Our framework incorporates a rich collection of mutation operators for product lines implemented in C to measure mutation scores of samples, including a novel family-based technique for product-line mutation detection. Our experimental results gained from applying our tool implementation to a collection of subject systems confirms the widely-accepted assumption that pairwise sampling constitutes the most reasonable efficiency/effectiveness trade-off for sample-based product-line testing.