Geant4 Maintainability Assessed with Respect to Software Engineering References

We report a methodology developed to quantitatively assess the maintainability of Geant4 with respect to software engineering references. The level of maintainability is determined by combining a set of metrics values whose references are documented in literature.Comment: 5 pages, 2 figures, 4 tables, IEEE NSS/MIC 201

Lettura Codici da Timbratrice nel Sistema di Gestione delle Presenze

Questo documento descrive l'attività svolta per permettere al Sistema di Gestione delle Presenze INFN di leggere i codici digitati nel marcatempo. Questa funzionalità non era stata prevista nel Sistema Presenze quando era gestito da un'azienda esterna, e quindi è stata interamente implementata dal personale della Direzione Sistemi Informativi. In sostanza, le specifiche richiedevano che il software leggesse un codice inserito nel marcatempo quando il badge veniva passato e creasse il giustificativo corrispondente nel cartellino, come se fosse stato inserito dalla pagina web. L'attività è stata completata nel 2018 e viene utilizzata da alcune strutture abilitate

Implementazione del lavoro agile nel sistema di gestione delle presenze

L’obiettivo di questo documento è descrivere il lavoro che è stato fatto per dotare il sistema di Gestione delle Presenze del giustificativo “Lavoro Agile”. Il lavoro è iniziato con l’analisi delle specifiche a metà novembre 2019 e una prima installazione del giustificativo è stata effettuata il 16/03/2020 in forma sperimentale e solo per un gruppo ristretto di strutture. L’estensione a tutte le strutture INFN è avvenuta il 15/05/2020. Da questa data in poi si sono succedute diverse installazioni per modifiche dei requisiti, introduzione di migliorie o soluzioni di bug. L’ultima installazione relativa a questo giustificativo è avvenuta il 24/03/2021. L’inserimento del “Lavoro Agile” nel sistema di Gestione delle Presenze ha assunto carattere di urgenza con lo scoppio della pandemia nel marzo 2020

A time tracking system for EU-funded INFN projects

The aim of this work is to describe the structure of a web application to support EU-funded INFN projects in the process of time tracking and reporting. The system has been designed in the early 2010s, following the growth of the EU-funded projects and the need for a framework to handle consistent reports according to the EU guidelines. One objective of this project is to guarantee and certify a valid distribution of hours declared on EU projects by INFN personnel in consistency with the official INFN clocking system, the scientific project management system and, overall, any signed agreements between the INFN and the founding bodies. The system allows the project participants to declare the hours worked on their projects, detailing their distribution across days, work packages and tasks. Interfaces are provided for Project Managers and Financial Officers to manage a project and monitor its trend. This activity is also supported by the INFN Business Intelligence Service, integrated with this system, to provide comprehensive reports for the upper management

A Preliminary Mapping Study of Software Metrics Thresholds

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Data Mining Techniques for Software Quality Prediction in Open Source Software

Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models

Data Mining Techniques for Software Quality Prediction in Open Source Software

Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models