Abstract
Data analysis has emerged as a cornerstone in facilitating informed decision-making across myriad fields, in particular in software development and project management. This integrative practice proves instrumental in enhancing operational efficiency, cutting expenditures, mitigating potential risks, and delivering superior results, all while sustaining structured organization and robust control. This paper presents ITC, a synergistic platform architected to streamline multi-organizational and multi-workspace collaboration for project management and technical documentation. ITC serves as a powerful tool, equipping users with the capability to swiftly establish and manage workspaces and documentation, thereby fostering the derivation of invaluable insights pivotal to both technical and business-oriented decisions. ITC boasts a plethora of features, from support for a diverse range of technologies and languages, synchronization of data, and customizable templates to reusable libraries and task automation, including data extraction, validation, and document automation. This paper also delves into the predictive analytics aspect of the ITC platform. It demonstrates how ITC harnesses predictive data models, such as Random Forest Regression, to anticipate project outcomes and risks, enhancing decision-making in project management. This feature plays a critical role in the strategic allocation of resources, optimizing project timelines, and promoting overall project success. In an effort to substantiate the efficacy and usability of ITC, we have also incorporated the results and feedback garnered from a comprehensive user assessment conducted in 2022. The feedback suggests promising potential for the platform's application, setting the stage for further development and refinement. The insights provided in this paper not only underline the successful implementation of the ITC platform but also shed light on the transformative impact of predictive analytics in information systems.

Abstract

Abstract
Quality assessment of open source software is becoming an important and active research area. One of the reasons for this recent interest is the consequence of Internet popularity. Nowadays, programming also involves looking for the large set of open source libraries and tools that may be reused when developing our software applications. In order to reuse such open source software artifacts, programmers not only need the guarantee that the reused artifact is certified, but also that independently developed artifacts can be easily combined into a coherent piece of software. In this paper we describe a domain specific language that allows programmers to describe in an abstract level how software artifacts can be combined into powerful software certification processes. This domain specific language is the building block of a web-based, open-source software certification portal. This paper introduces the embedding of such domain specific language as combinator library written in the Haskell programming language. The semantics of this language is expressed via attribute grammars that are embedded in Haskell, which provide a modular and incremental setting to define the combination of software artifacts.

Abstract

Abstract
The first version of the XIS profile addressed the development of interactive systems by defining models oriented only towards how the system should perform tasks. However, issues such as user-interface layouts, or the capture of interaction patterns, were not addressed by the profile, but only by the source-code generation process. This originated systems that, although functional, were considered by end-users as "difficult to use". In this paper we present the second version of the XIS UML profile, which is now a crucial component of the ProjectIT research project. This profile follows the "separation of concerns" principle by proposing an integrated set of views that address the various issues detected with the previous version of XIS. In addition, this profile also promotes the usage of extreme modeling, by relying on the extensive use of model-to-model transformation templates that are defined to accelerate the model development tasks. © 2007 IEEE.

Abstract