Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Deriving Models for Software Project Effort Estimation By Means of Genetic Programming

Software engineering, effort estimation, genetic programming, symbolic regression. This paper presents the application of a computational intelligence methodology in effort estimation for software projects. Namely, we apply a genetic programming model for symbolic regression; aiming to produce mathematical expressions that (1) are highly accurate and (2) can be used for estimating the development effort by revealing relationships between the project’s features and the required work. We selected to investigate the effectiveness of this methodology into two software engineering domains. The system was proved able to generate models in the form of handy mathematical expressions that are more accurate than those found in literature.

Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods

The job of software effort estimation is a critical one in the early stages of the software development life cycle when the details of requirements are usually not clearly identified. Various optimization techniques help in improving the accuracy of effort estimation. The Support Vector Regression (SVR) is one of several different soft-computing techniques that help in getting optimal estimated values. The idea of SVR is based upon the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. Further, the SVR kernel methods can be applied in transforming the input data and then based on these transformations, an optimal boundary between the possible outputs can be obtained. The main objective of the research work carried out in this paper is to estimate the software effort using use case point approach. The use case point approach relies on the use case diagram to estimate the size and effort of software projects. Then, an attempt has been made to optimize the results obtained from use case point analysis using various SVR kernel methods to achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information Processing (IJIP

Validation and Verification of Aircraft Control Software for Control Improvement

Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system