U.S. Software Protection: Problems of Trade Secret Estoppel under International and Brazilian Technology Transfer Regimes Note

This note describes the fundamental aspects of software protection and applies the requisites of U.S. trade secret protection to software. After explaining how the UNCTAD and Brazilian transfer of technology regimes apply to software licensing arrangements, this note argues that software distribution under these regimes estops U.S. trade secret protection by defeating the requisites of secrecy and competitive advantage. Specifically, the effects of the UNCTAD Draft International Code of Conduct on the Transfer of Technology (UNCTAD Code) and the Brazilian technology transfer regulations are analyzed to demonstrate the difficulties posed by legal regimes being considered and already in force in a number of developing countries. This note concludes with an analysis of some of the possibilities for protection of trade secrets with international softward distribution

The repository-based software engineering program: Redefining AdaNET as a mainstream NASA source

The Repository-based Software Engineering Program (RBSE) is described to inform and update senior NASA managers about the program. Background and historical perspective on software reuse and RBSE for NASA managers who may not be familiar with these topics are provided. The paper draws upon and updates information from the RBSE Concept Document, baselined by NASA Headquarters, Johnson Space Center, and the University of Houston - Clear Lake in April 1992. Several of NASA's software problems and what RBSE is now doing to address those problems are described. Also, next steps to be taken to derive greater benefit from this Congressionally-mandated program are provided. The section on next steps describes the need to work closely with other NASA software quality, technology transfer, and reuse activities and focuses on goals and objectives relative to this need. RBSE's role within NASA is addressed; however, there is also the potential for systematic transfer of technology outside of NASA in later stages of the RBSE program. This technology transfer is discussed briefly

Software patterns to improve knowledge transfer: an experiment.

Patterns for software development have been a hot topic for some time within the object-oriented community. Patterns are part of a software engineering problem-solving discipline. It all started with Design Patterns [11], but gradually patterns were used in a larger number of areas of system development. The goal of patterns within the software community is to create a body of literature to help software developers resolve recurring problems encountered throughout all areas of software development. Patterns help to create a shared language for communicating insight and experience about these problems and their solutions [4]. In this research report, first, a definition of software patterns is given, including some history, an overview of the different kinds of software patterns, the elements of a pattern and the different pattern formats. Secondly, as patterns claim to improve transfer of knowledge, we performed an experiment to test this hypothesis. This experiment is described in Section 2. Finally, Section 3 formulates the conclusions about this experiment.

Learning requirements engineering within an engineering ethos

An interest in educating software developers within an engineering ethos may not align well with the characteristics of the discipline, nor address the underlying concerns of software practitioners. Education for software development needs to focus on creativity, adaptability and the ability to transfer knowledge. A change in the way learning is undertaken in a core Software Engineering unit within a university's engineering program demonstrates one attempt to provide students with a solid foundation in subject matter while at the same time exposing them to these real-world characteristics. It provides students with a process to deal with problems within a metacognitive-rich framework that makes complexity apparent and lets students deal with it adaptively. The results indicate that, while the approach is appropriate, student-learning characteristics need to be investigated further, so that the two aspects of learning may be aligned more closely

A knowledge based software engineering environment testbed

The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processin

Bellwethers: A Baseline Method For Transfer Learning

Software analytics builds quality prediction models for software projects. Experience shows that (a) the more projects studied, the more varied are the conclusions; and (b) project managers lose faith in the results of software analytics if those results keep changing. To reduce this conclusion instability, we propose the use of "bellwethers": given N projects from a community the bellwether is the project whose data yields the best predictions on all others. The bellwethers offer a way to mitigate conclusion instability because conclusions about a community are stable as long as this bellwether continues as the best oracle. Bellwethers are also simple to discover (just wrap a for-loop around standard data miners). When compared to other transfer learning methods (TCA+, transfer Naive Bayes, value cognitive boosting), using just the bellwether data to construct a simple transfer learner yields comparable predictions. Further, bellwethers appear in many SE tasks such as defect prediction, effort estimation, and bad smell detection. We hence recommend using bellwethers as a baseline method for transfer learning against which future work should be comparedComment: 23 Page