1,720,986 research outputs found
Integrated Analysis Framework for Improving Production Processes in Software-intensive Systems
No full textAbstractSoftware-intensive systems involve the using of software engineering that cooperate with other engineering fields to achieve common goals, e.g., to provide good quality products to the customers in the right time. In the production automation systems as examples of software-intensive systems, the projects managers want to observe the production processes, so they can check the conformance between the running systems and the planned systems, e.g., whether the systems provide the expected products in the right time, how much time needed to finish a sequence of jobs is. However, the observation of production processes in these systems is difficult because heterogeneous data models are used to represent data from business and production layers. We propose an integrated analysis framework for improving production process in the production automation systems. Current results show that the framework can help the project manager to plan and conduct production process data collection and analysis for improving the process quality
Observing and Validating Heterogeneous Workflows in Multidisciplinary Engineering Environments
No full textMultidisciplinary engineering environments, such as production automation systems or power plants, typically involve different engineering domains, e.g., mechanical, elec-trical and software engineering. The stakeholders from dif-ferent engineering domains focus on their specific domain, aim at specific goals and follow different process steps by using different workflows, while have to work together to reach the project goal. In higher level, the project manager has a responsibility to manage the progress of project, in-cluding observing the using of workflows, e.g., for managing changes or deletion, in those different domains. The using of workflows in the project is not frequently occurred, but it can affect the progress of project, either make the project delayed, make the project faster, or even change the direc-tion of the project. Major challenges to handle the using of workflows in the multidisciplinary engineering projects are as follows, (1) the heterogeneity of data models, tools, and semantic to represent workflows is hard to manage, (2) the using of manual approach to collect the workflows data is time-consuming and error-prone, (3) different workflows that have relationships are not linked appropriately, make the analysis and validation hard. In this paper, we propose a framework to observe and validate different workflows for supporting project monitoring efforts by the project man-ager. We set up and implement different methods, tools, and procedures to collect, integrate and analyze different work-flows data. Major results show that the framework can sup-port project the manager in observing and validating work-flows more efficiently compared to traditional mainly man-ual approaches
Observability of Software Engineering Processes in Open Source Software Projects Domain
No full textOpen Source Software (OSS) projects as a complex software engineering system is an ideal domain for empirical software engineering, because it provides a lot of data and possibility to introduce new approaches that can be easily adapted and enrich methods to improve the software quality. As flexible and continuous developing systems, OSS projects are always growing as new requirements from users and new code from developers come into the project. However, at some point the project manager wants to know the status of OSS project whether the project is in the right direction and the product is delivered in a good quality. To get the immediate status of OSS project, some efforts have been done to observe the software engineering processes of OSS project. However, current approaches focus on limited areas of health indicators of OSS project. In this research, we will improve the approaches by proposing a framework that integrates different approaches on observing the software engineering processes and monitoring the health status of OSS project. Our objectives are to define observability factors of software engineering by making literature research on prior works and to assess the OSS engineering processes using the observability factors to show that this approach is working and can improve the software quality. In this research we use OSS projects domain as our context, and other engineering domains as references, e.g., production automation and (software+) engineering domains. Our contributions are improvement on the data collection and the data analysis steps
Project observation and analysis in heterogeneous software & systems development environments
Full text linkSoftware- und System-Entwicklungsprojekte brauchen oft die Expertise aus mehreren Engineering Disziplinen, etwa mechanischem, elektrischem und Software- Engineering. Diese Arbeit stellt das Projekt Beobachtungs- und Analyse Rahmenwerk vor, einen neuen Ansatz, der Projektmanager und Ingeneure dabei unterstützt unterschiedliche Engineering Prozesse in heterogenen Engineering Umgebungen systematisch zu vi beobachten und zu analysieren. Das POAF umfasst die Bereiche der Sammlung, Analyse und Präsentation von Daten aus heterogenen Software-Werkzeugumgebungen, und baut auf dem Semantic Web, statistischer Analyse und Process Mining auf, um verschiedene Methoden, Datenquellen sowie (semi-)automatisierte Werkzeuge bereitzustellen, die Projektmanager und Ingenieure bei Analyse- und Steuerungsaufgaben unterstützen.Die Forschungsergebnisse wurden im Rahmen von zwei industriellen Anwendungsbereichen, Open Source Software Engineering Projekten und Projekten für das Engineering von Automatisierungssystemen, hinsichtlich Machbarkeit, Effektivität und Effizienz evaluiert. Wesentliche Ergebnisse der Arbeit zeigen, dass die das Rahmenwerk nützlich war, und zumindest so effektive war und die Beobachtung und Analyse von Arbeitsabläufen effizienter unterstützt hat als traditionelle, vor allem manuelle, Ansätze.Software and systems development projects often depend on the expertise from multiple engineering domains, e.g., mechanical, electrical, and software engineering.This work introduces the Project Observation and Analysis Framework (POAF), a novel approach, which aims to support project managers and engineers in observing and analyzing engineering processes in heterogeneous engineering environments. The most important and novel contributions of POAF are (1) the semantic integration approach and integrated data model to support more efficient engineering process data collection and integration, (2) the using of combination of different analysis methods to strengthen the conclusion of the project status, and (3) the workflow validation cycle to support the conformance checking between the designed and the actual process model.The POAF consists of data collection, data analysis, and data presentation steps, and builds on semantic web, statistical analysis, and process mining technologies to provide a range of methods, data sources, and tools that help project managers and engineers to conduct analysis and control tasks. For example, in the power plant, the POAF is very useful in making project progress and risk monitoring and checking the conformance between the designed and the actual process model.The research results have been evaluated in two industrial application domains, namely open source software engineering projects and automated systems engineering projects, regarding feasibility, effectiveness and efficiency. Major results were that the framework was found useful, was at least as effective and supported more efficient workflow observation and analysis than traditional, mainly manual, approaches.<br /
Bridging Semantic Heterogeneities in Open Source Software Development Projects with Semantic Web Technologies
No full textThe semantic heterogeneity of Open Source Software (OSS) development projects comes from the using of different tools and models by the various stakeholders. These differences make the process of integration become difficult, since the project managers should recognize the different structure of the tools and models for analyzing the state of the projects. This manual analysis is costly and error prone. In this work we propose a seman-tic web technology approach to bridge these seman-tic heterogeneities, by using engineering knowledge base (EKB). The EKB enables mapping between local and domain ontology layers to allow querying the local tool knowledge using the domain-level knowledge and syntax. We empirically evaluate the feasibility of an EKB-based project monitoring system based on real-world data
Semantic Integration of Heterogeneous Data Sources for Monitoring Frequent-Release Software Projects
No full textOpen source software teams routinely develop complex
software products in frequent-release settings with rather
lightweight processes and project documentation. In this context
project a major challenge for data collection is how to
extract the relevant project management knowledge effectively
and efficiently from a wide range of software project data
sources, such as artifact versions, bug reports, and discussion
forums. In this paper we introduce a framework and tool support
for the semantic integration of data from a variety of data
sources to facilitate efficient data collection, even in projects
with frequent iterations. Based on data from real-world use
cases in open source projects we compare the efficiency of the
proposed framework with a traditional data warehouse approach.
Major result is that the proposed approach can make
data collection for project monitoring about 30% - 50% more
efficient, in particular, in contexts where heterogeneous data
sources change during the project
A Project Monitoring Cockpit Based On Integrating Data Sources in Open Source Software Development
No full textMany open source software (OSS) development
projects use tools and models that come from heterogeneous
sources. A project manager, who wants to analyze indicators for
the state of the project based on these data sources, faces the
challenge of how to link semi-structured information on common
concepts across heterogeneous data sources, e.g., source code
versions, mailing list entries, and bug reports. Unfortunately,
manual analysis is costly, error-prone, and often yields results
late for decision making. In this paper we propose linking OSS
data sources using semantic web technologies as foundation for
providing integrated indicators project status analysis. We introduce
the design concept of a project monitoring cockpit, Pro-
MonCo, and evaluate the feasibility and effectiveness with a prototype
for calculating communication metrics in a real-world
context, the Apache Tomcat project. Major result was that Pro-
MonCo efficiently supports frequent project monitoring by calculating
communication metrics based on semantically integrated
data originating from heterogeneous OSS project data sources
OSMF: A Framework for OSS Process Measurement
No full textPart 1: Information and Communication Technology- Eurasia Conference (ICT-EurAsia)International audienceAn Open Source Software (OSS) project can be considered as a new type of business entity involving various roles and stakeholders, e.g., project managers, developers, and users, who apply individual methods. The project managers have the responsibility to manage the OSS development in a way that the OSS product can be delivered to the customers in time and with good quality. This responsibility is challenging, because the heterogeneity of the data collected and analyzed from different stakeholders leads to the complexity of efforts of the project managers to measure and manage OSS projects. In this paper, we propose a measurement framework (OSMF) to enable the project managers to collect and analyze process data from OSS projects efficiently. Initial results show that OSMF can help project managers to manage OSS business processes more efficient, hence improve the decision on OSS project quality
Process Model Validation for Heterogeneous Engineering Environments
No full textHeterogeneous engineering environments involve multi-disciplinary engineering domains, e.g., mechanical, electrical and software engineering, to cooperate and reach the common goals, e.g., to produce a good quality of prod-ucts on time. The typical challenge that should be faced by the project manager in the heterogeneous engineering systems are how to validate heterogeneous processes in the systems, since the systems typically involve different type of processes from heterogeneous stakeholders. In this paper, we propose a busi-ness goal evaluation framework to improve a simulation-based process valida-tion (SbPV) to check the conformance between the designed process model and the actual production process data obtained from real-world industrial case. Ma-jor result was the business goal evaluation framework improved the efficiency of production process validation by providing information to support the project manager´s decision on the process improvement
Bridging Semantic Gaps Between Stakeholders in the Production Automation Domain with Ontology Areas
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