<p>Nowadays, assessing software quality and making predictions about the software are not</p><p>possible. Software metrics are useful tools for assessing software quality and for making</p><p>predictions. But currently the interpretation of the measured values is based on personal</p><p>experience. In order to be able to assess software quality, quantitative data has to be</p><p>obtained.</p><p>VizzAnalyzer is a program for analyzing open source Java Projects. It can be used</p><p>for collecting quantitative data for defining thresholds that can support the interpretation</p><p>of the measurement values. It helps to assess software quality by calculating over 20</p><p>different software metrics. I define a process for obtaining, storing and maintaining</p><p>software projects. I have used the defined process to analyze 60-80 software projects</p><p>delivering a large database with quantitative data.</p>

Nowadays, assessing software quality and making predictions about the software are not possible. Software metrics are useful tools for assessing software quality and for making predictions. But currently the interpretation of the measured values is based on personal experience. In order to be able to assess software quality, quantitative data has to be obtained. VizzAnalyzer is a program for analyzing open source Java Projects. It can be used for collecting quantitative data for defining thresholds that can support the interpretation of the measurement values. It helps to assess software quality by calculating over 20 different software metrics. I define a process for obtaining, storing and maintaining software projects. I have used the defined process to analyze 60-80 software projects delivering a large database with quantitative data.

An organizational strategy to develop software has appeared in the market. Organizations release software source code open and hope to attract volunteers to improve their software, forming what we call an open source project. Examples of organizations that have used this strategy include IBM (Eclipse), SAP (Netweaver) and Mozilla (Thunderbird). Moreover, thousands of these projects have been created as a consequence of the growing amount of software source code released by individuals. This expressive phenomenon deserves attention for its sudden appearance, newness and usefulness to public and private organizations. To explain the dynamics of open source projects, this research theoretically identified and empirically analyzed a construct – attractiveness – found crucial to them due to its influence on how they are populated and operate, subsequently impacting the qualities of the software produced and of the support provided. Both attractiveness' causes and consequences were put under scrutiny, as well as its indicators. On the side of the consequences, it was theoretically proposed and empirically tested whether the attractiveness of these projects affects their levels of activeness, efficiency, likelihood of task completion, and time for task completion, though not linearly, as task complexity could moderate the relationships between them. Also, it was argued at the theoretical level that activeness, efficiency, likelihood of task completion, and time for task completion mediate the relationship between attractiveness and software/support quality. On the side of attractiveness' causes, it was proposed and tested that five open software projects' characteristics (license type, intended audience, type of project and project’s life-cycle stage) impact attractiveness directly. Additionally, these projects' characteristics were argued to influence projects' levels of activeness, efficiency, likelihood of task completion, and time for task completion (and so an empirical evaluation of their associations was performed). The empirical tests of all these relationships between constructs were carried out using Structural Equation Modeling with Maximum Likelihood on three samples of over 4,600 projects each, collected from the largest repository of open source software, Sourceforge.net (a repeated cross-sectional approach). The results confirmed the importance of attractiveness, suggesting a direct influence on projects' dynamics, as opposed to the moderated-by-task complexity indirect paths first proposed. Furthermore, all four projects' characteristics studied were found to significantly influence projects' attractiveness, activeness, efficiency, likelihood of task completion, and time for task completion (with the exception of license type and time for task completion). Besides providing a statistical test of these propositions, this study discovered the direction of the influence of each project characteristic on projects' attractiveness, activeness, efficiency, likelihood of task completion and time for task completion. Lastly, conclusions, limitations, and future directions are discussed based on these findings.

<p>Software metrics are a useful tool for assessing software quality and for making predictions. But currently the interpretation of the measured values is based on personal experience and gut feeling. Not much information is available about thresholds and possible ranges of the metric values. In order to be able to define thresholds on which general recommendations could be based, quantitative data has to be obtain for allowing statistical evaluations and further investigations. So far the collection of test projects requires significant manual interaction for downloading and describing metadata.</p><p>This thesis describes a process for automatically obtaining, storing and maintaining a large number of open software projects from SourceForge.NET [1]. The projects are stored in a local folder structure; the meta-data is stored in a local database. The process is automated as far as possible, repeatable, transparent, extendible and flexible.</p>

Software metrics are a useful tool for assessing software quality and for making predictions. But currently the interpretation of the measured values is based on personal experience and gut feeling. Not much information is available about thresholds and possible ranges of the metric values. In order to be able to define thresholds on which general recommendations could be based, quantitative data has to be obtain for allowing statistical evaluations and further investigations. So far the collection of test projects requires significant manual interaction for downloading and describing metadata. This thesis describes a process for automatically obtaining, storing and maintaining a large number of open software projects from SourceForge.NET [1]. The projects are stored in a local folder structure; the meta-data is stored in a local database. The process is automated as far as possible, repeatable, transparent, extendible and flexible.