Abstract
ProcessPAIR is a novel tool for automating the performance analysis of software developers. 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. We present the results of a controlled experiment involving 61 software engineering master students, half of whom used ProcessPAIR in a performance analysis assignment. The results show significant benefits in terms of students' satisfaction (average score of 4.78 out of 5 for ProcessPAIR users, against 3.81 for other users), quality of the analysis outcomes (average grades achieved of 88.1 out of 100 for ProcessPAIR users, against 82.5 for other users), and time required to do the analysis (average of 252 min for ProcessPAIR users, against 262 min for other users, but with much room for improvement).

Abstract
High-maturity software development processes can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, conducting that analysis manually is challenging because of the potentially large amount of data to analyze and the effort and expertise required. In this paper, we present ProcessPAIR, a novel tool designed to help developers analyze their performance data with less effort, by automatically identifying and ranking performance problems and potential root causes, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be properly focused. The analysis is based on performance models defined manually by process experts and calibrated automatically from the performance data of many developers. We also show how ProcessPAIR was successfully applied for the Personal Software Process (PSP). A video about ProcessPAIR is available in https://youtu.be/dEk3fhhkduo.

Abstract
In previous work we proposed a performance analysis model for automatically identifying potential root causes of performance problems in personal software development. In this paper we present an approach for automatically ranking those potential root causes based on a cost-benefit estimate that takes into account historical data. The approach was applied for the Personal Software Process, taking advantage of a large data set referring to more than 30,000 projects, but can be replicated in other contexts.

Abstract
ProcessPAIR is a novel tool for helping software developers analyzing their personal performance. Based on a performance model calibrated from the anonymized performance data of many developers and the performance data submitted by an individual developer, it automatically identifies and ranks potential performance problems and their root causes for that developer. In this work we present WebProcessPAIR, which extends ProcessPAIR with the ability to recommend improvement actions to address the root causes identified, based on a crowdsourcing approach. A case study illustrates WebProcessPAIR usage. © 2017 Association for Computing Machinery.

Abstract
In a growing number of domains, such as health-care and transportation, several independent systems, forming a heterogeneous and distributed system of systems, are involved in the provisioning of end-to-end services to users. Testing such systems, running over interconnected mobile and cloud-based platforms, is particularly important and challenging, with little support being provided by current tools. In order to assess the current state of the practice regarding the testing of distributed and heterogeneous systems (DHS) and identify opportunities and priorities for research and innovation initiatives, we conducted an exploratory survey that was responded by 147 software testing professionals that attended industry-oriented software testing conferences, and present the main results in this paper. The survey allowed us to assess the relevance of DHS in software testing practice, the most important features to be tested in DHS, the current status of test automation and tool sourcing for testing DHS, and the most desired features in test automation solutions for DHS. We expect that the results presented in the paper are of interest to researchers, tool vendors and service providers in this field.

Abstract
Novel techniques and a toolset are presented for automatically testing the conformance of software implementations against partial behavioral models constituted by a set of parameterized UML sequence diagrams, describing both external interactions with users or client applications and internal interactions between objects in the system. Test code is automatically generated from the sequence diagrams and executed on the implementation under test, and test results and coverage information are presented back visually in the model. A runtime test library handles internal interaction checking, test stubs, and user interaction testing, taking advantage of aspect-oriented programming techniques. Incremental conformance checking is achieved by first translating sequence diagrams to Extended Petri Nets that combine the characteristics of Colored Petri Nets and Event-Driven Petri Nets.