Open source as a signalling device : an economic analysis

Open source projects produce goods or standards that do not allow for the appropriation of private returns by those who contribute to their production. In this paper we analyze why programmers will nevertheless invest their time and effort to code open source software. We argue that the particular way in which open source projects are managed and especially how contributions are attributed to individual agents, allows the best programmers to create a signal that more mediocre programmers cannot achieve. Through setting themselves apart they can turn this signal into monetary rewards that correspond to their superior capabilities. With this incentive they will forgo the immediate rewards they could earn in software companies producing proprietary software by restricting the access to the source code of their product. Whenever institutional arrangements are in place that enable the acquisition of such a signal and the subsequent substitution into monetary rewards, the contribution to open source projects and the resulting public good is a feasible outcome that can be explained by standard economic theory

Conditions for open source as a signalling device

Open source projects produce goods or standards that do not allow for the appropriation of private returns by those who contribute to their production. In this paper we analyze why programmers will nevertheless invest their time and effort to code open source software. We argue that the particular way in which open source projects are managed and especially how contributions are attributed to individual agents, allows the best programmers to create a signal that more mediocre programmers cannot achieve. Through setting themselves apart they can turn this signal into monetary rewards that correspond to their superior capabilities. With this incentive they will forgo the immediate rewards they could earn in software companies producing proprietary software by restricting the access to the source code of their product. Whenever institutional arrangements are in place that enable the acquisition of such a signal and the subsequent substitution into monetary rewards, the contribution to open source projects and the resulting public good is a feasible outcome that can be explained by standard economic theory

The natural history of bugs: using formal methods to analyse software related failures in space missions

Space missions force engineers to make complex trade-offs between many different constraints including cost, mass, power, functionality and reliability. These constraints create a continual need to innovate. Many advances rely upon software, for instance to control and monitor the next generation ‘electron cyclotron resonance’ ion-drives for deep space missions.Programmers face numerous challenges. It is extremely difficult to conduct valid ground-based tests for the code used in space missions. Abstract models and simulations of satellites can be misleading. These issues are compounded by the use of ‘band-aid’ software to fix design mistakes and compromises in other aspects of space systems engineering. Programmers must often re-code missions in flight. This introduces considerable risks. It should, therefore, not be a surprise that so many space missions fail to achieve their objectives. The costs of failure are considerable. Small launch vehicles, such as the U.S. Pegasus system, cost around $18 million. Payloads range from$4 million up to $1 billion for security related satellites. These costs do not include consequent business losses. In 2005, Intelsat wrote off$73 million from the failure of a single uninsured satellite. It is clearly important that we learn as much as possible from those failures that do occur. The following pages examine the roles that formal methods might play in the analysis of software failures in space missions

Report on a User Test and Extension of a Type Debugger for Novice Programmers

A type debugger interactively detects the expressions that cause type errors. It asks users whether they intend the types of identifiers to be those that the compiler inferred. However, it seems that novice programmers often get in trouble when they think about how to fix type errors by reading the messages given by the type debugger. In this paper, we analyze the user tests of a type debugger and report problems of the current type debugger. We then extend the type debugger to address these problems. Specifically, we introduce expression-specific error messages and language levels. Finally, we show type errors that we think are difficult to explain to novice programmers. The subjects of the user tests were 40 novice students belonging to the department of information science at Ochanomizu University.Comment: In Proceedings TFPIE 2014, arXiv:1412.473

Pair programming and the re-appropriation of individual tools for collaborative software development

Although pair programming is becoming more prevalent in software development, and a number of reports have been written about it [10] [13], few have addressed the manner in which pairing actually takes place [12]. Even fewer consider the methods used to manage issues such as role change or the communication of complex issues. This paper highlights the way resources designed for individuals are re-appropriated and augmented by pair programmers to facilitate collaboration. It also illustrates that pair verbalisations can augment the benefits of the collocated team, providing examples from ethnographic studies of pair programmers 'in the wild'

AI-Generated Fashion Designs: Who or What Owns the Goods?

As artificial intelligence (“AI”) becomes an increasingly prevalent tool in a plethora of industries in today’s society, analyzing the potential legal implications attached to AI-generated works is becoming more popular. One of the industries impacted by AI is fashion. AI tools and devices are currently being used in the fashion industry to create fashion models, fabric designs, and clothing. An AI device’s ability to generate fashion designs raises the question of who will own the copyrights of the fashion designs. Will it be the fashion designer who hires or contracts with the AI device programmer? Will it be the programmer? Or will it be the AI device itself? Designers invest a lot of talent, time, and finances into designing and creating each article of clothing and accessory it releases to the public; yet, under the current copyright standards, designers will not likely be considered the authors of their creations. Ultimately, this Note makes policy proposals for future copyright legislation within the United States, particularly recommending that AI-generated and AI-assisted designs be copyrightable and owned by the designers who purchase the AI device