Lack of risk management or its inadequate application is one reason that software development projects fail. Agile methods, which have become increasingly popular, do not offer specific activities to manage risks. We develop and evaluate a tool for managing risks in software development projects that use agile methods. The proposed tool provides a collection of risk management practices and an iterative lifecycle. To analyze the effectiveness of the proposed tool we conducted an experiment with the participation of experts in agile methods. Our tool increased the effectiveness of risk response planning without increasing the time invested in this process.

Risk management contributes to software projects success, but agile software development methods do not offer specific activities to manage risks. Therefore, this study aims to propose a list of risk management practices for agile projects, aiming to increase their chances of success. We analyzed 129 works on agile methods that afforded 127 risk management practices. We categorized and ranked practices using the AHP multi-criteria method with the participation of experts in the subject. The study presents risk management practices for daily meetings, increment, prototype, product backlog and Sprint planning as the most important for the risk management effectiveness. This study identified specific risk management practices for agile methods, not converging with other studies. Results contribute to the risk management improvement in agile projects and, consequently, increase their chances of success.

Studies show one of the reasons for the failure of the software projects is the absence of Risk Management or its improper application.The adoption of Scrum Framework on software projects is increasing.However, the Scrum does not have specific Risk Management activities.In this scenario, this paper presents the results of a survey applied using qualitative approach, in order to analyze how Risk Management is carried out in software projects, which use Scrum.The research method adopted was the case study and the research instrument for data collection was developed based on scientific articles and the application of structured interviews.As a result, this paper presents Risk Management practices which achieved greater and lower agreement among respondents and literature.It was found that Risk Management must be applied continuously in a feedback loop.Furthermore, Scrum projects must not have a high formal planning level, even for the high risk ones.This result does not converge to the literature.The research verified that the Risk Management in Scrum projects is performed differently from its application on traditional methodologies.The framework has native resources, but the use of the classic Risk Management processes must be incorporated and adapted to Scrum.

Although the Earned Value Management (EVM) technique has been used by several companies in various industrial sectors (software development, construction, aerospace, aeronautics, among others) for over 35 years to predict time and cost outcomes, many studies have found vulnerabilities, including: (i) cost performance data do not always have normal distribution, which makes reliable projections difficult; (ii) instability of cost performance indexes during the execution of projects, (iii) there is a worsening tendency in cost performance indexes when project approaches termination. This paper proposes an extension of the EVM technique through the integration of historical cost performance data of processes as a means to improve the project's cost predictability. The proposed technique was evaluated through an empirical study, which evaluated the implementation of the proposed technique in 22 software development projects. The proposed technique has been applied in real projects with the aim of evaluating the accuracy and variation compared to the traditional technique. Hypotheses tests with 95% significance level were performed, and the proposed technique was more accurate and more precise than the traditional technique for calculating the Cost Performance Index (CPI) and Estimates at Completion (EAC).

This paper proposes an extension of the Earned Value Management — EVM technique through the integration of historical cost performance data of processes under statistical control as a means to improve the predictability of the cost of projects. The proposed technique was evaluated through a case-study in industry, which evaluated the implementation of the proposed technique in 22 software development projects Hypotheses tests with 95% significance level were performed, and the proposed technique was more accurate and more precise than the traditional technique for calculating the Cost Performance Index — CPI and Estimates at Completion — EAC.