Development of large projects often involves parallel work performed in multiple branches. Eventually, these branches need to be reintegrated through a merge operation. During merge, conflicts may arise and developers need to communicate to reach consensus about the desired resolution. For this reason, including the right developers to a collaborative merge session is fundamental. However, this task can be difficult especially when many different developers have made significant changes on each branch over a large number of files. In this paper, we present TIPMerge, an approach designed to recommend participants for collaborative merge sessions. TIPMerge analyzes the project history and builds a ranked list of developers who are the most appropriate to integrate a pair of branches (Developer Ranking) by considering developers' changes in the branches, in the previous history, and in the dependencies among files across branches. Simply selecting the top developers in such a ranking is easy, but is not effective for collaborative merge sessions as the top developers may have overlapping knowledge. To support collaborative merge, TIPMerge employs optimization techniques to recommend developers with complementary knowledge (Team Recommendation) aiming to maximize joint knowledge coverage. Our results show a mean normalized improvement of 49.5% (median 50.4%) for the joint knowledge coverage with the optimization techniques for assembling teams of three developers for collaborative merge in comparison to choosing the top-3 developers in the ranked list.

The evaluation of dependability or performance of general systems usually relies on the assistance of stochastic modeling and simulation tools. Those software packages enables the creation of models and computation of metrics quickly and accurately. This paper introduces the Mercury tool, which is an integrated software that enables creating and evaluating Reliability Block Diagrams, Stochastic Petri Nets, Continuous Time Markov Chains, and Energy Flow Models. Mercury provides a graphical user interface, a script language for command-line interface, and also an API (Application Programming Interface) that enables interaction through external applications. The evaluation of models is not restricted to the assumption of Exponential distributions, which is a common constraint in other similar tools. Mercury implements a simulation framework that allows more than 25 probability distributions, as well as a moment matching method that enables expolynomial -phase-type -distributions for models solved through numerical analysis. This paper presents the main features and methods available in Mercury to aid the dependability and performance evaluation of various systems, for both academy and industry. The accuracy and applicability of the tool is illustrated by a case study of packet loss and throughput for a Video on Demand (VoD) system.

Parallel development in branches is a common software practice. However, past work has found that integration of changes across branches is not easy, and often leads to failures. Thus far, there has been little work to recommend developers who have the right expertise to perform a branch integration. We propose TIPMerge, a novel tool that recommends developers who are best suited to perform merges, by taking into consideration developers’ past experience in the project, their changes in the branches, and de-pendencies among modified files in the branches. We evaluated TIPMerge on 28 projects, which included up to 15,584 merges with at least two developers, and potentially conflicting changes. On average, 85% of the top-3 recommendations by TIPMerge correctly included the developer who performed the merge. Best (accuracy) results of recommendations were at 98%. Our inter-views with developers of two projects reveal that in cases where the TIPMerge recommendation did not match the actual merge developer, the recommended developer had the expertise to per-form the merge, or was involved in a collaborative merge session.

Development in large projects often involves branches, where changes are performed in parallel and merged periodically. This merge process often combines two independent and long sequences of commits that may have been performed by multiple, different developers. It is nontrivial to identify the right developer to perform the merge, as the developer must have enough understanding of changes in both branches to ensure that the merged changes comply with the objective of both lines of work (branches), which may have been active for several months. We designed and developed TIPMerge, a novel tool that recommends developers who are best suited to perform the merge between two given branches. TIPMerge does so by taking into consideration developers’ past experience in the project, their changes in the branches, and the dependencies among modified files in the branches. In this paper we demonstrate TIPMerge over a real merge case from the Voldemort project.

Business service through the Internet, particularly the cloud computing paradigm, have demanded more computational resources from data centers to provide high-availability services. As consequence, issues such as operational costs and sustainability increase, mainly, due to the redundant infrastructures needed to support the data center operation. However, data center designers have slightly information on the sustainability impact, operational cost and availability of data center architectures. In this context, models are important to support data center designers to estimate the environmental impact, dependability as well as the cost associated to the infrastructure before implementing it. This paper presents a set of formal models for estimating sustainability impact and dependability metrics, supported by an integrated environment, namely, ASTRO.

The number and size of data centers have greatly increased in recent years, mainly, due to the explosive growth of Internet services, such as those based on cloud computing paradigm. Therefore, service providers have intensified efforts on improving the quality of services while minimizing the costs of data centers, for instance, those associated to out-of-services periods. In other words, robust data center infrastructures are necessary, such that dependability requirements are satisfied while simultaneously incurring costs as minimal as possible. This paper proposes an approach that combines reliability importance indexes with TCA (Total Cost of Acquisition) to evaluate data center power infrastructures. The proposed approach aims at indicating the benefits and drawbacks of different architectures for power infrastructures to guide the decision maker in evaluating the most appropriate design.