Abstract

We develop a model of innovation communities which allows us to address in a systematic way the influence of users and developers as well as communication between and within these groups. Based on this model, we derive a formal approach to quantify communication flows, community activity and community turnover. These measures are calculated using the data of 100 open source software projects. Our empirical analysis shows that: (i) Users play indeed a predominant role in communication, which points towards the vivid role of an active user community; (ii) communication is highly concentrated, which points towards the importance of active individuals and (iii) community turnover exhibits only little correlation with community segregation, which may allow to benefit from high turnover rates while keeping negative effects small. We argue that insight from this extensive analysis not only complements existing case studies, it also provides a reference frame to put these singular results into perspective when aiming at generalizations.

Abstract

Efficient bug triaging procedures are an important precondition for successful collaborative software engineering projects. Triaging bugs can become a laborious task particularly in open source software (OSS) projects with a large base of comparably inexperienced part-time contributors. In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away. Our classification is based on nine measures to quantify the social embeddedness of bug reporters in the collaboration network. We demonstrate its applicability in a case study, using a comprehensive data set of more than 700, 000 bug reports obtained from the BUGZILLA installation of four major OSS communities, for a period of more than ten years. For those projects that exhibit the lowest fraction of valid bug reports, we find that the bug reporters’ position in the collaboration network is a strong indicator for the quality of bug reports. Based on this finding, we develop an automated classification scheme that can easily be integrated into bug tracking platforms and analyze its performance in the considered OSS communities. A support vector machine (SVM) to identify valid bug reports based on the nine measures yields a precision of up to 90.3% with an associated recall of 38.9%. With this, we significantly improve the results obtained in previous case studies for an automated early identification of bugs that are eventually fixed. Furthermore, our study highlights the potential of using quantitative measures of social organization in collaborative software engineering. It also opens a broad perspective for the integration of social network analysis in the design of support infrastructures.

Abstract

We analyse the relation between the emotions and the activity of contributors in the Open Source Software project Gentoo. Our case study builds on extensive data sets from the project's bug tracking platform Bugzilla, to quantify the activity of contributors, and its mail archives, to quantify the emotions of contributors by means of sentiment analysis. The Gentoo project is known for a considerable drop in development performance after the sudden retirement of a central contributor. We analyse how this event correlates with the negative emotions, both in bilateral email discussions with the central contributor, and at the level of the whole community of contributors. We then extend our study to consider the activity patters on Gentoo contributors in general. We find that contributors are more likely to become inactive when they express strong positive or negative emotions in the bug tracker, or when they deviate from the expected value of emotions in the mailing list. We use these insights to develop a Bayesian classifier that detects the risk of contributors leaving the project. Our analysis opens new perspectives for measuring online contributor motivation by means of sentiment analysis and for real-time predictions of contributor turnover in Open Source Software projects.

Abstract

Social organization and division of labor crucially influence the performance of collaborative software engineering efforts. In this paper, we provide a quantitative analysis of the relation between social organization and performance in Gentoo, an Open Source community developing a Linux distribution. We study the structure and dynamics of collaborations as recorded in the project's bug tracking system over a period of ten years. We identify a period of increasing centralization after which most interactions in the community were mediated by a single central contributor. In this period of maximum centralization, the central contributor unexpectedly left the project, thus posing a significant challenge for the community. We quantify how the rise, the activity as well as the subsequent sudden dropout of this central contributor affected both the social organization and the bug handling performance of the Gentoo community. We analyze social organization from the perspective of network theory and augment our quantitative findings by interviews with prominent members of the Gentoo community which shared their personal insights.

Abstract

Software development depends on many factors, including technical, human and social aspects. Due to the complexity of this dependence, a unifying framework must be defined and for this purpose we adopt the complex networks methodology. We use a data-driven approach based on a large collection of open source software projects extracted from online project development platforms. The preliminary results presented in this article reveal that the network perspective yields key insights into the sustainability of software development.

Abstract

We investigate the relationship between class dependency and change propagation (cochange) in software written in Java. On the one hand, we find a strong correlation between dependency and cochange. Furthermore, we provide empirical evidence for the propagation of change along paths of dependency. These findings support the often alleged role of dependencies as propagators of change. On the other hand, we find that approximately half of all dependencies are never involved in cochanges and that the vast majority of cochanges pertain to only a small percentage of dependencies. This means that inferring the cochange characteristics of a software architecture solely from its dependency structure results in a severely distorted approximation of cochange characteristics. Any metric which uses dependencies alone to pass judgment on the evolvability of a piece of Java software is thus unreliable. As a consequence, we suggest to always take both the change characteristics and the dependency structure into account when evaluating software architecture.

Abstract

Based on the analysis of the dependency network in 18 Java projects, we develop a novel model of network growth which considers both preferential attachment and the addition of new nodes with a heterogeneous distribution of their initial degree, k0. Empirically we find that the cumulative distributions of initial and final degrees in the network follow power law behaviours: 1−P(k0)∝k1−$α$ as a function of the network size, we find empirically K(N)∝N$β$,where $β$ ∈[1.25, 2] (for small N), while converging to $β$ ∼1 for large N. This indicates a transition from a growth regime with increasing network density towards a sustainable regime, which prevents a collapse due to 0 ,and 1−P(k)∝k1−$γ$, respectively. For the total number of links ever increasing dependencies. Our theoretical framework allows us to predict relations between the exponents $α$, $β$, $γ$, which also link issues of software engineering and developer activity. These relations are verified by means of computer simulations and empirical investigations. They indicate that the growth of real Open Source Software networks occurs on the edge between two regimes, which are dominated either by the initial degree distribution of added nodes, or by the preferential attachment mechanism. Hence, the heterogeneous degree distribution of newly added nodes, found empirically, is essential to describe the laws of sustainable growth in networks.

Abstract

In this paper we investigate the relationship between class dependency and change propagation in Java software. By analyzing 35 large Open Source Java projects, we find that in the majority of the projects more than half of the dependencies are never involved in change propagation. Furthermore, our analysis shows that only a few dependencies are transmitting the majority of change propagation events. An additional analysis reveals that this concentration cannot be explained by the different ages of the dependencies. The conclusion is that the dependency structure alone is a poor measure for the change dynamics. This contrasts with current literature.

Abstract

Trees are a special sub-class of networks with unique properties, such as the level distribution which has often been overlooked.We analyse a general tree growth model proposed by Klemm et al. [Phys. Rev. Lett. 95, 128701 (2005)] to explain the growth of user-generated directory structures in computers. The model has a single parameter q which interpolates between preferential attachment and random growth. Our analysis results in three contributions: first, we propose a more efficient estimation method for q based on the degree distribution, which is one specific representation of the model. Next, we introduce the concept of a level distribution and analytically solve the model for this representation. This allows for an alternative and independent measure of q.We argue that, to capture real growth processes, the q estimations from the degree and the level distributions should coincide. Thus, we finally apply both representations to validate the model with synthetically generated tree structures, as well as with collected data of user directories. In the case of real directory structures, we show that q measured from the level distribution are incompatible with q measured from the degree distribution. In contrast to this, we find perfect agreement in the case of simulated data. Thus, we conclude that the model is an incomplete description of the growth of real directory structures as it fails to reproduce the level distribution. This insight can be generalised to point out the importance of the level distribution for modeling tree growth.