A model and framework for reliable build systems

Reliable and fast builds are essential for rapid turnaround during development and testing. Popular existing build systems rely on correct manual specification of build dependencies, which can lead to invalid build outputs and nondeterminism. We outline the challenges of developing reliable build systems and explore the design space for their implementation, with a focus on non-distributed, incremental, parallel build systems. We define a general model for resources accessed by build tasks and show its correspondence to the implementation technique of minimum information libraries, APIs that return no information that the application doesn't plan to use. We also summarize preliminary experimental results from several prototype build managers

A Nested Attention Neural Hybrid Model for Grammatical Error Correction

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography

ARIES: Acquisition of Requirements and Incremental Evolution of Specifications

This paper describes a requirements/specification environment specifically designed for large-scale software systems. This environment is called ARIES (Acquisition of Requirements and Incremental Evolution of Specifications). ARIES provides assistance to requirements analysts for developing operational specifications of systems. This development begins with the acquisition of informal system requirements. The requirements are then formalized and gradually elaborated (transformed) into formal and complete specifications. ARIES provides guidance to the user in validating formal requirements by translating them into natural language representations and graphical diagrams. ARIES also provides ways of analyzing the specification to ensure that it is correct, e.g., testing the specification against a running simulation of the system to be built. Another important ARIES feature, especially when developing large systems, is the sharing and reuse of requirements knowledge. This leads to much less duplication of effort. ARIES combines all of its features in a single environment that makes the process of capturing a formal specification quicker and easier

Communications in Choreographies, Revisited

Choreographic Programming is a paradigm for developing correct-by-construction concurrent programs, by writing high-level descriptions of the desired communications and then synthesising process implementations automatically. So far, choreographic programming has been explored in the monadic setting: interaction terms express point-to-point communications of a single value. However, real-world systems often rely on interactions of polyadic nature, where multiple values are communicated among two or more parties, like multicast, scatter-gather, and atomic exchanges. We introduce a new model for choreographic programming equipped with a primitive for grouped interactions that subsumes all the above scenarios. Intuitively, grouped interactions can be thought of as being carried out as one single interaction. In practice, they are implemented by processes that carry them out in a concurrent fashion. After formalising the intuitive semantics of grouped interactions, we prove that choreographic programs and their implementations are correct and deadlock-free by construction

JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction

We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding. We describe the types of corrections made and benchmark four leading GEC systems on this corpus, identifying specific areas in which they do well and how they can improve. JFLEG fulfills the need for a new gold standard to properly assess the current state of GEC.Comment: To appear in EACL 2017 (short papers

Architectural mismatch tolerance

The integrity of complex software systems built from existing components is becoming more dependent on the integrity of the mechanisms used to interconnect these components and, in particular, on the ability of these mechanisms to cope with architectural mismatches that might exist between components. There is a need to detect and handle (i.e. to tolerate) architectural mismatches during runtime because in the majority of practical situations it is impossible to localize and correct all such mismatches during development time. When developing complex software systems, the problem is not only to identify the appropriate components, but also to make sure that these components are interconnected in a way that allows mismatches to be tolerated. The resulting architectural solution should be a system based on the existing components, which are independent in their nature, but are able to interact in well-understood ways. To find such a solution we apply general principles of fault tolerance to dealing with arch itectural mismatche

Artificial morality: Making of the artificial moral agents

Abstract: Artificial Morality is a new, emerging interdisciplinary field that centres around the idea of creating artificial moral agents, or AMAs, by implementing moral competence in artificial systems. AMAs are ought to be autonomous agents capable of socially correct judgements and ethically functional behaviour. This request for moral machines comes from the changes in everyday practice, where artificial systems are being frequently used in a variety of situations from home help and elderly care purposes to banking and court algorithms. It is therefore important to create reliable and responsible machines based on the same ethical principles that society demands from people. New challenges in creating such agents appear. There are philosophical questions about a machine’s potential to be an agent, or mora l agent, in the first place. Then comes the problem of social acceptance of such machines, regardless of their theoretic agency status. As a result of efforts to resolve this problem, there are insinuations of needed additional psychological (emotional and cogn itive) competence in cold moral machines. What makes this endeavour of developing AMAs even harder is the complexity of the technical, engineering aspect of their creation. Implementation approaches such as top- down, bottom-up and hybrid approach aim to find the best way of developing fully moral agents, but they encounter their own problems throughout this effort