As Artificial Intelligence (AI) techniques become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use.

Ways of applying artificial intelligence in software engineering

As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses Only by doing so can we devise tools and solutions to help mitigate them This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use

Ways of Applying Artificial Intelligence in Software Engineering

Natural language interfaces are becoming more and more common, because they are powerful and easy to use. Examples of such interfaces are voice controlled navigation devices, Apple’s personal assistant Siri, Google Voice Search, and translation services. However, such interfaces are extremely difficult to build, to maintain, and to port to new domains. We present an approach for building and porting such interfaces quickly. NLCI is a natural language command interpreter that accepts action commands in English and translates them into executable code. The core component is an ontology that models an API. Once the API is “ontologized”, NLCI translates input sentences into sequences of API calls that implement the intended actions. Two radically different APIs were ontologized: openHAB for home automation and Alice for building 3D animations. Construction of the ontology can be automated if the API uses descriptive names for its components. In that case, the language interface can be generated completely automatically. Recall and precision of NLCI on a benchmark of 50 input scripts are 67 and 78 %, resp. Though not yet acceptable for practical use, the results indicate that the approach is feasible. NLCI accepts typed input only. Future work will use a speech front-end to test spoken input.

/pdf/nlci-a-natural-language-command-interpreter-39eetghyxy.pdf

NLCI: a natural language command interpreter

This article describes a series of collaborative experiences carried out using hypermedia educational material that includes Augmented Reality (AR) activities to teach control structures in initial Programming courses. EPRA (which is the name of the hypermedia educational material developed) is described, and the results obtained are analyzed in relation to student satisfaction and perception of the knowledge acquired using this material and the related experience. One of the main findings is that the proposed Augmented Reality activities are considered by students to favor learning of the topic at hand and motivate them, adding a fun element to the abstract concepts involved.

Augmented Reality for Programming Teaching. Student Satisfaction Analysis

Mainly focused on solving abstraction problems, programming paradigms limit language expressiveness, thus leaving unexplored natural language descriptions that are implicitly expressive. Several authors have developed tools for programming with a natural language subset limited to specific domains to deal with the ambiguity occurring with artificial intelligence technique use. This article presents a review of tools and languages with naturalistic features and highlights the problems that authors have resolved and those they have not addressed, going on to discuss the fact that a “naturalistic” language based on a well-defined model is not reported.

A Survey of Naturalistic Programming Technologies

We investigate how natural languages such as English can be used as programming languages. Often, in natural language (as well as in programming) different actions happen at the same time or are repeated. In natural language we just say what is going to happen -- in programming we use control structures. Story lines without equivalent language/phrase structures are unnatural and programs without control structures are hard to read. An empirical study showed how users express control structures in natural language. We propose a new and automatic text analysis. It leverages Stanford's typed dependencies to detect sentence structures that imply strictly sequential control flows, repetition, and parallelism. The technique is analyzed in the context of Alice, a 3D programming environment, and Alice NLP, a system for programming Alice in ordinary English. We evaluate our approach with 52 texts with 795 control-flow-affected elements in total and show that 82% of these elements can be detected successfully. We performed a second evaluation with manually corrected input and find that our approach successfully detects the control structures 97% of the time in the absence of parser errors.

Text understanding for programming in natural language: control structures

This contribution proposes web-based concept completion, an approach for enriching concept descriptions created from observations with additional semantic information. The proposed methods are designed as an extension to the Object-Oriented World Model (OOWM), a system for environment representation in cognitive systems using semantic knowledge captured in a domain model. An adaptive version of the OOWM allows extending the domain model with new concept descriptions created from observations. Such sensor observations yet only provide the perceptual information part of a concept description and lack semantic information. To this end, this contribution is concerned with selecting and integrating suitable resources readily available from the World Wide Web into a demonstrator for concept completion.

Towards Web-Based Semantic Knowledge Completion for Adaptive World Modeling in Cognitive Systems

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Text understanding for programming in natural language: control structures

Towards Web-Based Semantic Knowledge Completion for Adaptive World Modeling in Cognitive Systems