Myths and Realities about Online Forums in Open Source Software Development: An Empirical Study

The use of free and open source software (OSS) is gaining momentum due to the ever increasing availability and use of the Internet. Organizations are also now adopting open source software, despite some reservations, in particular regarding the provision and availability of support. Some of the biggest concerns about free and open source software are post release software defects and their rectification, management of dynamic requirements and support to the users. A common belief is that there is no appropriate support available for this class of software. A contradictory argument is that due to the active involvement of Internet users in online forums, there is in fact a large resource available that communicates and manages the provision of support. The research model of this empirical investigation examines the evidence available to assess whether this commonly held belief is based on facts given the current developments in OSS or simply a myth, which has developed around OSS development. We analyzed a dataset consisting of 1880 open source software projects covering a broad range of categories in this investigation. The results show that online forums play a significant role in managing software defects, implementation of new requirements and providing support to the users in open source software and have become a major source of assistance in maintenance of the open source projects

Machine learning stochastic design models.

Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study