Abstract
To ensure interoperability and the correct behavior of heterogeneous distributed systems in key scenarios, it is important to conduct automated integration tests, based on distributed test components (called local testers) that are deployed close to the system components to simulate inputs from the environment and monitor the interactions with the environment and other system components. We say that a distributed test scenario is locally controllable and locally observable if test inputs can be decided locally and conformance errors can be detected locally by the local testers, without the need for exchanging coordination messages between the test components during test execution (which may reduce the responsiveness and fault detection capability of the test harness). DCO Analyzer is the first tool that checks if distributed test scenarios specified by means of UML sequence diagrams exhibit those properties, and automatically determines a minimum number of coordination messages to enforce them. The demo video for DCO Analyzer can be found at https://youtu.be/LVIusK36_bs.

Abstract
Forecasts of the effort or delivery date can play an important role in managing software projects, but the estimates provided by development teams are often inaccurate and time-consuming to produce. This is not surprising given the uncertainty that underlies this activity. This work studies the use of Monte Carlo simulations for generating forecasts based on project historical data. We have designed and run experiments comparing these forecasts against what happened in practice and to estimates provided by developers, when available. Comparisons were made based on the mean magnitude of relative error (MMRE). We did also analyze how the forecasting accuracy varies with the amount of work to be forecasted and the amount of historical data used. To minimize the requirements on input data, delivery date forecasts for a set of user stories were computed based on takt time of past stories (time elapsed between the completion of consecutive stories); effort forecasts were computed based on full-time equivalent (FTE) hours allocated to the implementation of past stories. The MMRE of delivery date forecasting was 32% in a set of 10 runs (for different projects) of Monte Carlo simulation based on takt time. The MMRE of effort forecasting was 20% in a set of 5 runs of Monte Carlo simulation based on FTE allocation, much smaller than the MMRE of 134% of developers' estimates. A better forecasting accuracy was obtained when the number of historical data points was 20 or higher. These results suggest that Monte Carlo simulations may be used in practice for delivery date and effort forecasting in agile projects, after a few initial sprints. © 2021 ACM.

Abstract
Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, particularly with the application of machine learning techniques to help identify risk levels or risk factors of a project before the project development begins, with the intent of improving the likelihood of success of software projects. This paper provides an overview of various concepts related to risk and risk management in software projects, including traditional techniques used to identify and control risks in software projects, as well as machine learning techniques and methods which have been applied to provide better estimates and classification of the risk levels and risk factors that can be encountered during the development of a software project. The paper also presents an analysis of machine learning oriented risk management studies and experiments found in the literature as a way of identifying the type of inputs and outputs, as well as frequent algorithms used in this research area.

Abstract
Over the past decades, software engineering has reached a level of maturity which entails great challenges in its education. Universities must prepare students to real-life challenges by offering courses to aid students in developing several vital skills which go beyond hard skills (e.g., communication skills and self-management). At the Faculty of Engineering of the University of Porto, a pioneering course, dubbed Project Management Laboratory, offers the proper environment for students to develop such skills by inviting industry to be closely involved in the education of the students. This course integrates practice and theory in a setting close to what the students will face when they move into industry. This paper reports on the experience, results, and benefits of this innovative course.

Abstract
Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, especially with the application of machine learning techniques to help identify risk levels of risk factors of a project before its development begins, with the goal of improving the likelihood of success of these projects. This paper presents the results of the application of machine learning techniques for risk assessment in software projects. A Python application was developed and, using Scikit-learn, two machine learning models, trained using software project risk data shared by a partner company of this project, were created to predict risk impact and likelihood levels on a scale of 1 to 3. Different algorithms were tested to compare the results obtained by high performance but non-interpretable algorithms (e.g., Support Vector Machine) and the ones obtained by interpretable algorithms (e.g., Random Forest), whose performance tends to be lower than their non-interpretable counterparts. The results showed that Support Vector Machine and Naive Bayes were the best performing algorithms. Support Vector Machine had an accuracy of 69% in predicting impact levels, and Naive Bayes had an accuracy of 63% in predicting likelihood levels, but the results presented in other evaluation metrics (e.g., AUC, Precision) show the potential of the approach presented in this use case.

Abstract
Large language model (LLM)-powered assistants are increasingly used for generating program code and unit tests, but their application in acceptance testing remains underexplored. To help address this gap, this paper explores the use of LLMs for generating executable acceptance tests for web applications through a two-step process: (i) generating acceptance test scenarios in natural language (in Gherkin) from user stories, and (ii) converting these scenarios into executable test scripts (in Cypress), knowing the HTML code of the pages under test. This two-step approach supports acceptance test-driven development, enhances tester control, and improves test quality. The two steps were implemented in the AutoUAT and Test Flow tools, respectively, powered by GPT-4 Turbo, and integrated into a partner company's workflow and evaluated on real-world projects. The users found the acceptance test scenarios generated by AutoUAT helpful 95% of the time, even revealing previously overlooked cases. Regarding Test Flow, 92% of the acceptance test cases generated by Test Flow were considered helpful: 60% were usable as generated, 8% required minor fixes, and 24% needed to be regenerated with additional inputs; the remaining 8% were discarded due to major issues. These results suggest that LLMs can, in fact, help improve the acceptance test process, with appropriate tooling and supervision.