Past research established that individuals joining an Open Source community typically follow a socialization process called "the onion model": newcomers join a project by first contributing at the periphery through mailing list discussions and bug trackers and as they develop skill and reputation within the community they advance to central roles of contributing code and making design decisions. However, the modern Open Source landscape has fewer projects that operate independently and many projects under the umbrella of software ecosystems that bring together projects with common underlying components, technology, and social norms. Participants in such an ecosystems may be able to utilize a significant amount of transferrable knowledge when moving between projects in the ecosystem and, thereby, skip steps in the onion model. In this paper, we examine whether the onion model of joining and progressing in a standalone Open Source project still holds true in large project ecosystems and how the model might change in such settings.

The onion patch: migration in open source ecosystems

A programming interface for a computer platform can include various functionality. In certain embodiments, the programming interface includes one or more of a first group of services related to re-usable user interface controls, a second group of services related to user interface dialogs and user interface wizards, a third group of services related to extending the user interface functionality, and a fourth group of services related to extending functionality of a desktop of the user interface.

Programming Interface for a Computer Platform

It is widely believed that most Recognition and Mining (RM) workloads can easily take advantage of parallel computing platforms because these workloads are dataparallel. Contrary to this popular belief, we present RM workloads for which conventional parallel implementations scale poorly on multi-core platforms. We identify off-chip memory transfers and overheads in the parallel runtime library as the primary bottlenecks that limit speedups to be well below the ideal linear speedup expected for data-parallel workloads. To achieve improved parallel scalability, we identify and exploit several interesting properties of RM workloads — sparsity of model updates, low spatial locality among model updates, presence of insignificant computations, and the inherently self-healing nature of these algorithms in the presence of errors. We leverage these domain-specific characteristics to improve parallel scalability in two major ways. First, we utilize data dependency relaxation to simultaneously execute multiple training iterations in parallel, thereby increasing the granularity of the parallel tasks and significantly lowering the run-time overheads of fine-grained threading. Second, we strategically drop selected computations that are insignificant to the accuracy of the final result, but account for a disproportionately large amount of off-chip (memory and coherence) traffic. Through the application of the proposed techniques, we show that much higher speedups are possible on multi-core platforms for two important RM applications — document search using semantic indexing, and eye detection in images using generalized learning vector quantization. On an 8-core platform, we achieve application speedups of 5.5X and 7.3X compared to sequential implementations. Compared to conventional parallel implementations of these applications using Intel's TBB, the proposed techniques result in 4.3X and 4.9X improvements. Although the optimized parallel implementations are not numerically equivalent to the sequential implementations, the output quality is shown to be comparable (and within the margin of variation produced by processing the input data in a different order). We also explore error mitigation techniques that can be used to ensure that the accuracy of results is not compromised.

Exploiting the forgiving nature of applications for scalable parallel execution

Characterizing the performance of scientific applications is essential for effective code optimization, both by compilers and by high-level adaptive numerical algorithms. While maximizing power efficiency is becoming increasingly important in current high-performance architectures, little or no hardware or software support exists for detailed power measurements. Hardware counter-based power models are a promising method for guiding software-based techniques for reducing power. We present a component-based infrastructure for performance and power modeling of parallel scientific applications. The power model leverages on-chip performance hardware counters and is designed to model power consumption for modern multiprocessor and multicore systems. Our tool infrastructure includes application components as well as performance and power measurement and analysis components. We collect performance data using the TAU performance component and apply the power model in the performance and power analysis of a PETSc-based parallel fluid dynamics application by using the PerfExplorer component.

/pdf/a-component-infrastructure-for-performance-and-power-1o62x39vtj.pdf

A component infrastructure for performance and power modeling of parallel scientific applications

Software engineering is still a relatively young field, struggling to develop consistent standards and methods across the domain. For a given project, developers can choose from dozens of models, tools, platforms, and languages for specification, design, implementation, and testing. The globalization of software engineering and the rise of Open Source further complicate the issues as firms now must collaborate and coordinate with other firms and individuals possessing a myriad of goals, norms, values, expertise, and preferences. This thesis uses four empirical studies to take a vertical examination of Open Source ecosystems and identify the way that foundations, firms, and individuals come together to create large scale software ecosystems and produce world class software despite their differing goals and values. 
First, I examine Open Source as a collaborative phenomenon between firms and non-profit foundations that support many communities and identify the ways in which non-profit foundations enable member firms to create value in the ecosystem. Next, an empirical study of direct collaboration between firms within the Eclipse system reveals that most firms operate relatively independently, but there is still heavy reliance on a single dominant player for core portions of the ecosystem. I then evaluate how the presence of commercial firms affects the attraction and retention of volunteer developers in an Open Source community. The final study examines how individual developers manage their dependencies in Open Source and extends the socio-technical congruence metric to address changing requirements and facilitate the metric as a tool for continual use. Finally, based on the findings of these studies, I close with a set of recommendations for stakeholders investing in Open Source.

/pdf/vertical-interaction-in-open-software-engineering-qmiqqekmn3.pdf

Vertical interaction in open software engineering communities

All those descriptions currently surround Eclipse, the language-agnostic code base that’s billed as a “universal platform.” According to its creators it’s more precisely a metaplatform. That is, it’s a building tool out of which developers can create IDEs, or indeed anything else they might desire.

https://dl.acm.org/doi/pdf/10.1145/966712.966727

Eclipse: A Platform Becomes an Open-Source Woodstock: Call it a platform. Call it a tool. Call it the hottest open-source software movement since Linux.

Even though the frenzied hype over Java has died down since the Internet bubble burst, Java is becoming hugely popular in the wireless space. Several events highlight its emergence. Most recently, in December, Texas Instruments opened a research operation in France to focus on the integration of Java apps into the next generation of wireless devices.

https://dl.acm.org/doi/pdf/10.1145/971564.971585

Toolkit: Java is Jumpin’: There’s perception, and then there’s reality.

This month we’re embedding ourselves inside Microsoft’s .NET Compact Framework (CF), billed by Microsoft as the perfect platform to create software applications that target mobile devices. That’s accurate—and it’s not.

Microsoft’s Compact Framework Targets Smart Devices: Welcome to my first installment of ACM Queue’s ToolKit column. Each issue I’ll dig beneath the market-friendly, feature-rich exterior of some of the best-known (and some of the least-known) development tools in an attempt to separate the core app from the product spec sheet.

FSF was founded in 1984 by Richard Stallman. Then a programmer at MIT’s Artificial Intelligence Laboratory, Stallman resigned to protest its restrictive copyright policy. He started the GNU (GNU’s Not Unix) Project, an effort to build a free Unix clone, and wrote the GNU (General Public License), which essentially said you could redistribute GNU code for free as long as you also gave away any modification you added—and the free software movement was on its way.

https://dl.acm.org/doi/pdf/10.1145/966789.966795

Toolkit: GNU Tools: Still Relevant?: Often lost amid the focus on software you don’t have to pay for - such as Linux and Eclipse - is any mention of the organization that started it all: the Free Software Foundation (FSF).

Call it all-tools-for-all-things-Microsoft. That’s the way I’m beginning to think about Longhorn, Microsoft’s next-generation operating system, expected in 2006. The operating system will herald more than just the transition of mainstream computing to 64 bits. It will mark the emergence of a new, multifaceted programming model. Breaking away from traditional computer science taxonomy, where operating systems stood barely removed from the hardware abstraction layer, Longhorn will be more like a multi-tentacled container for a wide range of administration and application tools.

Longhorn Ties Platform Apps to Core Operating System: Will Microsoft’s New OS Be a Developer’s Dream-Come-True?

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Eclipse: A Platform Becomes an Open-Source Woodstock: Call it a platform. Call it a tool. Call it the hottest open-source software movement since Linux.

Toolkit: Java is Jumpin’: There’s perception, and then there’s reality.

Toolkit: GNU Tools: Still Relevant?: Often lost amid the focus on software you don’t have to pay for - such as Linux and Eclipse - is any mention of the organization that started it all: the Free Software Foundation (FSF).

Longhorn Ties Platform Apps to Core Operating System: Will Microsoft’s New OS Be a Developer’s Dream-Come-True?