Development of a System for Analysing Method Names in Java Source Code

Naming code can seem like a simple task, however finding a good name can be rather challenging. Entity names should be consistent and brief yet comprehensive when representing the information each entity hold. What is considered a good name can be highly debatable, although it usually involves descriptive names that can contribute to readability and comprehensibility of source code. Bad code names can cause uncertainty, potential future bugs and be misleading. For this reason, the task of naming code is vital, hence there is a need of a system to improve and maintain it. To develop such a system, there are requirements required to be specified to define the expected implementation for certain entity names. These requirements are encoded into software in a domain-specific language, granting executable code to be generated from the expressed requirements. As a result, this name analysis tool provides programmers to perform code analysis on Java source code checking if the entities act in accordance with the requirements of their names. Additionally, the result shows insights of how contributions from linguistics can be valuable for software development and can be used to analyse software languages, such as entity names.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Community-Driven Language Development

International audienceSoftware development processes are becoming more collaborative, trying to integrate end-users as much as possible. The idea is to advance towards a community- driven process where all actors (both technical and non-technical) work together to ensure that the system-to-be will satisfy all expectations. This seems specially appropriate in the field of Domain-Specific Languages (DSLs) typically designed to facilitate the development of software for a particular domain. DSLs offer constructs closer to the vocabulary of the domain which simplifies the adoption of the DSL by end-users. Interestingly enough, the development of DSLs is not a collaborative process itself. In this sense, the goal of this paper is to propose a collaborative infrastructure for the development of DSLs where end-users have a direct and active participation in the evolution of the language. This infrastructure is based on Collaboro, a DSL to represent change proposals, possible solutions and comments arisen during the development and evolution of a language

Development of Q&A systems using AcQA

In order to help the user to search for relevant information, Question and Answering (Q&A) Systems provide the possibility to formulate the question freely in a natural language, retrieving the most appropriate and concise answers. These systems interpret the user question to understand his information needs and return him the more adequate replies in a semantic sense; they do not perform a statistical word search like happens in the existing search engines. There are several approaches to developing and deploying Q&A Systems, making it hard to choose the best way to build the system. To turn easier this process, we are proposing a way to automatically create Q&A Systems (AcQA) based on DSLs, thus allowing the setup and the validation of the Q&A System independent of the implementation techniques. With our proposal (AcQA language), we want the developers to focus on the data and contents, instead of implementation details. We conducted an experiment to assess the feasibility of using AcQA. The study was carried out with people from the computer science field and shows that our language simplifies the development of a Q&A System.info:eu-repo/semantics/publishedVersio

Model-Driven Engineering for Artificial Intelligence - A Systematic Literature Review

Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 703 candidate studies, eventually retaining 15 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study's findings show that the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM \textit{Business Understanding} phase, are rarely reflected. Conclusion: The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area

DoMoRe – A recommender system for domain modeling

Domain modeling is an important activity in early phases of software projects to achieve a shared understanding of the problem field among project participants. Domain models describe concepts and relations of respective application fields using a modeling language and domain-specific terms. Detailed knowledge of the domain as well as expertise in model-driven development is required for software engineers to create these models. This paper describes DoMoRe, a system for automated modeling recommendations to support the domain modeling process. We describe an approach in which modeling benefits from formalized knowledge sources and information extraction from text. The system incorporates a large network of semantically related terms built from natural language data sets integrated with mediator-based knowledge base querying in a single recommender system to provide context-sensitive suggestions of model elements

Meta-tools for software language engineering : a flexible collaborative modeling language for efficient telecommunications service design

International audienceThe increasingly competitive environment pressures telecommunications service providers to reduce their concept-to-market time. This time is influenced by a multitude of factors. For the benefit of telecom service designers, this paper focuses on increasing the degree of automation, offering team collaboration capabilities and bridging heterogeneous technologies. To address these factors, we propose a model-based meta-tool approach, which rapidly and iteratively generates particular tools for software languages. Each language is specific to one of the viewpoints involved in the definition of a service, as identified in the Intelligent Network Conceptual Model. A flexible language prototype for service designers, that blends a higher degree of formality with creative freedom, has already been implemented. The integration of first collaboration capabilities, defined and tooled, into this language, by including the rationale behind the designers' decisions, is currently being pursued. A second language prototype, for network designers, together with syntactic and semantic (partial) automatic interoperability between these two viewpoints, are also proposed

Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041