Abstract
High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Personal Software Process (PSP) and the Team Software Process (TSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. Currently, there are several tools that automate data collection and produce performance charts for manual analysis in the context of the PSP/TSP, but practically no tool support exists for automating the data analysis and the recommendation of improvement actions. Manual analysis of this performance data is problematic because of the large amount of data to analyze and the time and expertise required. Hence, we propose in this paper a performance model and a tool (named PSP PAIR) to automate the analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and recommend improvement actions. The work presented is limited to the analysis of the time estimation performance of PSP developers, but is extensible to other performance indicators and development processes.

Abstract
This paper proposes a new approach to reduce the effort of building formal models representative of the structure and behaviour of Graphical User Interfaces (GUI). The main goal is to automatically extract the GUI model with a dynamic reverse engineering process, consisting in an exploration phase, that extracts information by interacting with the GUI, and in a model generation phase that, making use of machine learning techniques, uses the extracted information of the first step to generate a state-machine model of the GUI, including guard conditions to remove ambiguity in transitions. © 2012 IEEE.

Abstract
Model-driven engineering approaches aim at avoiding productivity, model quality and model maintenance problems that arise when models are used for documentation only, by generating executable applications from models. However, in many cases, the level of detail of the models needed to generate complete applications is too much or only effective for specific domains. For those cases where it is not practical to build complete models and generate complete applications from them, we propose a lightweight approach, applicable at different levels (unit, integration and system testing), that combines partial application generation from structural models with test generation from partial behavioral models. To demonstrate the approach, we developed a plug-in that adds to the code generation capabilities of an existing UML modeling tool, the capability of generating executable tests from sequence diagrams acting also as parameterized test scenarios, including some novel features as compared to existing model-based testing tools.

Abstract
CMMI practices can be poorly implemented leading to weak performance gain. SCAMPI verifies model compliance but not performance. Hence, a framework to evaluate the quality of implementation of each practice, based on compliance and performance results, will prevent poor implementation, locate and fix problems, and ultimately achieve better results. In this paper we propose such a framework, based on a combination of leading and lagging indicators measuring compliance, efficiency and efficacy. © 2012 Springer-Verlag.

Abstract
Several approaches exist to automatically derive test cases that check the conformance of the implementation of abstract data types (ADTs) with respect to their specification. However, they lack support for the testing of implementations of ADTs defined by generic classes. In this paper, we present a novel technique to automatically derive, from specifications, unit test cases for Java generic classes that, in addition to the usual testing data, encompass implementations for the type parameters. The proposed technique relies on the use of Alloy Analyzer to find model instances for each test goal. JUnit test cases and Java implementations of the parameters are extracted from these model instances. © 2012 Springer-Verlag.

Abstract