Abstract
ProcessPAIR is a novel method and tool for automating the performance analysis in software development. Based on performance models structured by process experts and calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. However, the current calibration method is not fully automatic, because, in the case of performance indicators that affect other indicators in a conflicting way, the process expert has to manually calibrate the optimal value in a way that balances those impacts. In this paper we propose a novel method to automate this step, taking advantage of training data sets. We demonstrate the feasibility of the method with an example related with the Code Review Rate indicator, with conflicting impacts on Productivity and Quality.

Abstract

Abstract
To ensure interoperability and the correct end-to-end behavior of heterogenous distributed systems, it is important to conduct integration tests that verify the interactions with the environment and between the system components in key scenarios. The automation of such integration tests requires that test components are also distributed, with local testers deployed close to the system components, coordinated by a central tester. In such a test architecture, it is important to maximize the autonomy of the local testers to minimize the communication overhead and maximize the fault detection capability. A test scenario is called locally observable and locally controllable, if conformance errors can be detected locally and test inputs can be decided locally, respectively, by the local testers, without the need for exchanging coordination messages between the test components during test execution (i.e., without any communication overhead). For test scenarios specified by means of UML sequence diagrams that don’t exhibit those properties, we present in this paper an approach with tool support to automatically find coordination messages that, added to the given scenario, make it locally controllable and locally observable. © Springer Nature Switzerland AG 2019.

Abstract
Collecting product and process measures in software development projects, particularly in education and training environments, is important as a basis for assessing current performance and opportunities for improvement. However, analyzing the collected data manually is challenging because of the expertise required, the lack of benchmarks for comparison, the amount of data to analyze, and the time required to do the analysis. ProcessPAIR is a novel tool for automated performance analysis and improvement recommendation; based on a performance model calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. In education and training environments, it increases students' autonomy and reduces instructors' effort in grading and feedback. In this article, we present the results of a controlled experiment involving 61 software engineering master students, half of whom used ProcessPAIR in a Personal Software Process (PSP) performance analysis assignment, and the other half used a traditional PSP support tool (Process Dashboard) for performing the same assignment. The results show significant benefits in terms of students' satisfaction (average score of 4.78 in a 1-5 scale for ProcessPAIR users, against 3.81 for Process Dashboard users), quality of the analysis outcomes (average grades achieved of 88.1 in a 0-100 scale for ProcessPAIR users, against 82.5 for Process Dashboard users), and time required to do the analysis (average of 252 min for ProcessPAIR users, against 262 min for Process Dashboard users, but with much room for improvement).

Abstract
Cloud computing has emerged as the de facto approach for providing services over the Internet. Although having increased popularity, challenges arise in the management of such environments, especially when the cloud service providers are constantly evolving their services and technology stack in order to maintain position in a demanding market. This usually leads to a combination of different services, each one managed individually, not providing a big picture of the architecture. In essence, the end state will be too many resources under management in an overwhelming heterogeneous environment. An infrastructure that has considerable growth will not be able to avoid its increasing complexity. Thus, this papers introduces liveness as an attempt to increase the feedback-loop to the developer in the management of cloud architectures. This aims to ease the process of developing and integrating cloud-based systems, by giving the possibility to understand the system and manage it in an interactive and immersive experience, thus perceiving how the infrastructure reacts to change. This approach allows the real-time visualization of a cloud infrastructure composed of a set of Amazon Web Services resources, using visual city metaphors.

Abstract
Any software system that has a considerable growing number of features will suffer from essential complexity, which makes the understanding of the software artifacts increasingly costly and time-consuming. A common approach for reducing the software understanding complexity is to use software visualizations techniques. There are already several approaches for visualizing software, as well as for extracting the information needed for those visualizations. This paper presents a novel approach to tackle the software complexity, delving into the common approaches for extracting information about software artifacts and common software visualization metaphors, allowing users to dive into the software system in a live way using virtual reality (VR). Experiments were carried out in order to validate the correct extraction of metadata from the software artifact and the corresponding VR visualization. With this work, we intend to present a starting point towards a Live Software Development approach.