Semantically Resolving Type Mismatches in Scientific Workflows

Scientists are increasingly utilizing Grids to manage large data sets and execute scientific experiments on distributed resources. Scientific workflows are used as means for modeling and enacting scientific experiments. Windows Workflow Foundation (WF) is a major component of Microsoft’s .NET technology which offers lightweight support for long-running workflows. It provides a comfortable graphical and programmatic environment for the development of extended BPEL-style workflows. WF’s visual features ease the syntactic composition of Web services into scientific workflows but do nothing to assure that information passed between services has consistent semantic types or representations or that deviant flows, errors and compensations are handled meaningfully. In this paper we introduce SAWSDL-compliant annotations for WF and use them with a semantic reasoner to guarantee semantic type correctness in scientific workflows. Examples from bioinformatics are presented

Shared visiting in Equator city

In this paper we describe an infrastructure and prototype system for sharing of visiting experiences across multiple media. The prototype supports synchronous co-visiting by physical and digital visitors, with digital access via either the World Wide Web or 3-dimensional graphics

Pragmas: Literal Messages as Powerful Method Annotations

International audienceOften tools need to be extended at runtime depending on the availability of certain features. Simple registration mechanisms can handle such a situation: It often boils down to represent an action and describe such action with some meta-data. However, ad-hoc registration mechanisms have some drawbacks: they are often not uniform and do not fit well with code navigability. In addition, metadata is not automatically synchronized with the data or behavior it describes. In this article we present the notion of pragmas, method annotations , as it was introduced in VisualWorks and now it is an important extensibility mechanism of Pharo. We present some examples of pragmas within Pharo

Leveraging Component-Based Software Engineering with Fraclet

International audienceComponent-based software engineering has achieved wide acceptance in the domain of software engineering by improving productivity, reusability and composition. This success has also encouraged the emergence of a plethora of component models. Nevertheless, even if the abstract models of most of lightweight component models are quite similar, their programming models can still differ a lot. This drawback limits the reuse and composition of components implemented using different programming models. The contribution of this article is to introduce Fraclet as a programming model com- mon to several lightweight component models. This programming model is presented as an annotation framework, which allows the developer to annotate the program code with the elements of the abstract component model. Then, using a generative approach, the annotated program code is completed according to the programming model of the component model to be supported by the component runtime environment. This article shows that this annotation framework provides a significant simplification of the program code by removing all dependencies on the component model interfaces. These benefits are illustrated with the Fractal and OpenCOM component models

On Leveraging Tests to Infer Nullable Annotations

Issues related to the dereferencing of null pointers are a pervasive and widely studied problem, and numerous static analyses have been proposed for this purpose. These are typically based on dataflow analysis, and take advantage of annotations indicating whether a type is nullable or not. The presence of such annotations can significantly improve the accuracy of null checkers. However, most code found in the wild is not annotated, and tools must fall back on default assumptions, leading to both false positives and false negatives. Manually annotating code is a laborious task and requires deep knowledge of how a program interacts with clients and components. We propose to infer nullable annotations from an analysis of existing test cases. For this purpose, we execute instrumented tests and capture nullable API interactions. Those recorded interactions are then refined (santitised and propagated) in order to improve their precision and recall. We evaluate our approach on seven projects from the spring ecosystems and two google projects which have been extensively manually annotated with thousands of @Nullable annotations. We find that our approach has a high precision, and can find around half of the existing @Nullable annotations. This suggests that the method proposed is useful to mechanise a significant part of the very labour-intensive annotation task

The AutoProof Verifier: Usability by Non-Experts and on Standard Code

Formal verification tools are often developed by experts for experts; as a result, their usability by programmers with little formal methods experience may be severely limited. In this paper, we discuss this general phenomenon with reference to AutoProof: a tool that can verify the full functional correctness of object-oriented software. In particular, we present our experiences of using AutoProof in two contrasting contexts representative of non-expert usage. First, we discuss its usability by students in a graduate course on software verification, who were tasked with verifying implementations of various sorting algorithms. Second, we evaluate its usability in verifying code developed for programming assignments of an undergraduate course. The first scenario represents usability by serious non-experts; the second represents usability on "standard code", developed without full functional verification in mind. We report our experiences and lessons learnt, from which we derive some general suggestions for furthering the development of verification tools with respect to improving their usability.Comment: In Proceedings F-IDE 2015, arXiv:1508.0338

A Programming Model for Hybrid Workflows: combining Task-based Workflows and Dataflows all-in-one

This paper tries to reduce the effort of learning, deploying, and integrating several frameworks for the development of e-Science applications that combine simulations with High-Performance Data Analytics (HPDA). We propose a way to extend task-based management systems to support continuous input and output data to enable the combination of task-based workflows and dataflows (Hybrid Workflows from now on) using a single programming model. Hence, developers can build complex Data Science workflows with different approaches depending on the requirements. To illustrate the capabilities of Hybrid Workflows, we have built a Distributed Stream Library and a fully functional prototype extending COMPSs, a mature, general-purpose, task-based, parallel programming model. The library can be easily integrated with existing task-based frameworks to provide support for dataflows. Also, it provides a homogeneous, generic, and simple representation of object and file streams in both Java and Python; enabling complex workflows to handle any data type without dealing directly with the streaming back-end.Comment: Accepted in Future Generation Computer Systems (FGCS). Licensed under CC-BY-NC-N

Bringing ultra-large-scale software repository mining to the masses with Boa

Mining software repositories provides developers and researchers a chance to learn from previous development activities and apply that knowledge to the future. Ultra-large-scale open source repositories (e.g., SourceForge with 350,000+ projects, GitHub with 250,000+ projects, and Google Code with 250,000+ projects) provide an extremely large corpus to perform such mining tasks on. This large corpus allows researchers the opportunity to test new mining techniques and empirically validate new approaches on real-world data. However, the barrier to entry is often extremely high. Researchers interested in mining must know a large number of techniques, languages, tools, etc, each of which is often complex. Additionally, performing mining at the scale proposed above adds additional complexity and often is difficult to achieve. The Boa language and infrastructure was developed to solve these problems. We provide users a domain-specific language tailored for software repository mining and allow them to submit queries via our web-based interface. These queries are then automatically parallelized and executed on a cluster, analyzing a dataset containing almost 700,000 projects, history information from millions of revisions, millions of Java source files, and billions of AST nodes. The language also provides an easy to comprehend visitor syntax to ease writing source code mining queries. The underlying infrastructure contains several optimizations, including query optimizations to make single queries faster as well as a fusion optimization to group queries from multiple users into a single query. The latter optimization is important as Boa is intended to be a shared, community resource. Finally, we show the potential benefit of Boa to the community by reproducing a previously published case study and performing a new case study on the adoption of Java language features

Debugging Woven Code

The ability to debug woven programs is critical to the adoption of Aspect Oriented Programming (AOP). Nevertheless, many AOP systems lack adequate support for debugging, making it difficult to diagnose faults and understand the program's structure and control flow. We discuss why debugging aspect behavior is hard and how harvesting results from related research on debugging optimized code can make the problem more tractable. We also specify general debugging criteria that we feel all AOP systems should support. We present a novel solution to the problem of debugging aspect-enabled programs. Our Wicca system is the first dynamic AOP system to support full source-level debugging of woven code. It introduces a new weaving strategy that combines source weaving with online byte-code patching. Changes to the aspect rules, or base or aspect source code are rewoven and recompiled on-the-fly. We present the results of an experiment that show how these features provide the programmer with a powerful interactive debugging experience with relatively little overhead