Event-Based Monitoring of Open Source Software Projects (original) (raw)

A Project Monitoring Cockpit Based On Integrating Data Sources in Open Source Software Development

2010

Abstract—Many 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, eg, source code versions, mailing list entries, and bug reports. Unfortunately, manual analysis is costly, error-prone, and often yields results late for decision making.

Non-invasive Investigation of the Software Process in Open Source Projects

2006

Abstract. Open Source and Agile (eXtreme Programming, in particular) projects have several commonalities such as focus on the value for the user, continuous feedback, high level of communication, etc. Moreover, both approaches present difficulties in keeping track of the status of the development, verifying the quality of the production process, identifying best practices, etc. Such difficulties are related to the lack of a formal activity for the collection of data regarding the development process.

Observability of Software Engineering Processes in Open Source Software Projects Domain

ABSTRACT Open Source Software (OSS) projects as a complex software engineering system is a rich domain for empirical software studies, where we can apply many different approaches on vast amounts of OSS project data that can be accessed easily and open for public, set different goals, questions and metrics to improve the software quality. As a flexible and continuously developing system, OSS projects are always growing as the users give new requirements and the developers put new codes to the projects.

Measurement of Processes in Open Source Software Development

Trends in Information Management, 2012

Purpose: This paper attempts to present a set of basic metrics which can be used to measure basic development processes in an OSS environment. Design/Methodology/Approach: Reviewing the earlier literature helped in exploring the metrics for measuring the development processes in OSS environment. Results: The OSSD is different from traditional software development because of its open development environment. The development processes are different and the measures required to assess them have to be different.

Observations on patterns of development in open source software projects

Proceedings of the fifth workshop on Open source software engineering - 5-WOSSE, 2005

This paper discusses a project aimed at understanding how open source software evolves by examining patterns of development and changes in releases over time. The methodological approach of the research and initial observations are described. These include descriptions of release cycles and categorization of projects based on the overall changes in size and complexity exhibited across releases. Implications of these observations are discussed in light of prior and future work on understanding OSS evolution.

Experiences mining open source release histories

Proceeding of the 2nd workshop on Software engineering for sensor network applications - SESENA '11, 2011

Software releases form an important part of the life cycle of a software project. Typically, each project produces releases in its own way, using various methods of versioning, archiving, announcing and publishing the release. Understanding the release history of a software project can shed light on the project history, as well as the release process used by that project, and how those processes change. However, many factors make automating the retrieval of release history information difficult, such as the many sources of data, a lack of relevant standards and a disparity of tools used to create releases. In spite of the large amount of raw data available, no attempt has been made to create a release history database of a large number of projects in the open source ecosystem. This paper presents our experiences, including the tools, techniques and pitfalls, in our early work to create a software release history database which will be of use to future researchers who want to study and model the release engineering process in greater depth.

Locating Bug IDs and Development Logs in Open Source Software (OSS) projects: An Experience Report

2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2018

The development logs of software projects, contained in Version Control (VC) systems can be severely incomplete when tracking bugs, especially in open source projects, resulting in a reduced traceability of defects. Other times, such logs can contain bug information that is not available in bug tracking system (BT system) repositories, and vice-versa: if development logs and BT system data were used together, researchers and practitioners often would have a larger set of bug IDs for a software project, and a better picture of a bug life cycle, its evolution and maintenance. Considering a sample of 10 OSS projects and their development logs and BT systems data, the two objectives of this paper are (i) to determine which of the keywords 'Fix', 'Bug' or the '#' identifier provide the better precision; and (ii) to analyse their respective precision and recall at locating the larger amount possible of bug IDs manually. Overall, our results suggest that the use of the '#' identifier in conjunction with the bug ID digits (e.g., #1234) is more precise for locating bugs in development logs, than the use of the 'Bug' and 'Fix' keywords. Such keywords are indeed present in the development logs, but they are less useful when trying to connect the development actions with the bug traces in software project.

DISCOVERING, MODELING, AND RE-ENACTING OPEN SOURCE SOFTWARE DEVELOPMENT PROCESSES: A CASE STUDY

New Trends in Software Process Modelling, 2006

Software process discovery has historically been a labor and time intensive task, either done through exhaustive empirical studies or in an automated fashion using techniques such as logging and analysis of command shell operations. While empirical studies have been fruitful, data collection has proven to be tedious and time consuming. Existing automated approaches have very detailed, low level but not rich results. We are interested in process discovery in large, globally distributed organizations such as the NetBeans open source software development community, which currently engages over twenty thousand developers distributed over several continents working collaboratively, sometimes across several stages of the software lifecycle in parallel. This presents a challenge for those who want to join the community and participate in, as well as for those who want to understand these processes. This chapter discusses our efforts to discover selected open source processes in the NetBeans community. We employ a number of data gathering techniques ranging from ethnographic to semi-structured to formal, computational models, which were fed back to the community for further evaluation. Along the way, we discuss collecting, analyzing, and modeling the data, as well as lessons learned from our experiences.

Automated classification of change messages in open source projects

ACM Symposium on Applied Computing, 2008

Source control systems permit developers to attach a free form message to every committed,change. The content of these change messages,can support software maintenance activities. We present an automated,approach to classify a change message as either a bug fix, a feature introduction, or a general maintenance,change. Researchers can study the evolution of project using our classification. For ex- ample, researchers

Analysis of Open Source Software Development Iterations by Means of Burst Detection Techniques

2009

A highly efficient bug fixing process and quick release cycles are considered key properties of the open source software development methodology. In this paper, we study the relation between code activities (such as lines of code added per commit), bug fixing activities, and software release dates in a subset of open source projects. To study the phenomenon, we gathered a large data set about the evolution of 5 major open source projects.