Identificação de Experiências da Adoção de Learning Analytics no Ensino de Engenharia de Software

O número de cursos de graduação e especialização em Engenharia de Software tem crescido nos últimos anos no Brasil. Atualmente, as instituições já dispõem de dados sobre a execução destes cursos que poderiam ser utilizados na análise de sua condução e identificação de melhorias. Uma possibilidade de avaliar este grande volume de dados pode ser por meio de Learning Analytics. Entretanto, existem poucos trabalhos ainda nesta área. Este trabalho exploratório-descritivo tem o objetivo de identificar experiências do uso de Learning Analytics no ensino de Engenharia de Software e fomentar a discussão deste tema. Foram encontrados poucos trabalhos neste contexto, o que demonstra a carência de estudos de Learning Analytics nesta área

Software Development Analytics in Practice: A Systematic Literature Review

Context:Software Development Analytics is a research area concerned with providing insights to improve product deliveries and processes. Many types of studies, data sources and mining methods have been used for that purpose. Objective:This systematic literature review aims at providing an aggregate view of the relevant studies on Software Development Analytics in the past decade (2010-2019), with an emphasis on its application in practical settings. Method:Definition and execution of a search string upon several digital libraries, followed by a quality assessment criteria to identify the most relevant papers. On those, we extracted a set of characteristics (study type, data source, study perspective, development life-cycle activities covered, stakeholders, mining methods, and analytics scope) and classified their impact against a taxonomy. Results:Source code repositories, experimental case studies, and developers are the most common data sources, study types, and stakeholders, respectively. Product and project managers are also often present, but less than expected. Mining methods are evolving rapidly and that is reflected in the long list identified. Descriptive statistics are the most usual method followed by correlation analysis. Being software development an important process in every organization, it was unexpected to find that process mining was present in only one study. Most contributions to the software development life cycle were given in the quality dimension. Time management and costs control were lightly debated. The analysis of security aspects suggests it is an increasing topic of concern for practitioners. Risk management contributions are scarce. Conclusions:There is a wide improvement margin for software development analytics in practice. For instance, mining and analyzing the activities performed by software developers in their actual workbench, the IDE

Automatic Recall of Lessons Learned for Software Project Managers

Lessons learned (LL) records constitute a software organization’s memory of successes and failures. LL are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manually searching this repository is a daunting task, so it is often overlooked. This can lead to the repetition of previous mistakes and missing potential opportunities, which, in turn, can negatively affect the organization’s profitability and competitiveness. In this thesis, we present a novel solution that provides an automatic process to recall relevant LL and to push them to project managers. This substantially reduces the amount of time and effort required to manually search the unstructured LL repositories, and therefore, it encourages the utilization of LL. In this study, we exploit existing project artifacts to build the LL search queries on-the-fly, in order to bypass the tedious manual search process. While most of the current LL recall studies rely on case-based reasoning, they have some limitations including the need to reformat the LL repository, which is impractical, and the need for tight user involvement. This makes us the first to employ information retrieval (IR) to address the LL recall. An empirical study has been conducted to build the automatic LL recall solution and evaluate its effectiveness. In our study, we employ three of the most popular IR models to construct a solution that considers multiple classifier configurations. In addition, we have extended this study by examining the impact of the hybridization of LL classifiers on the classifiers’ performance. Furthermore, a real-world dataset of 212 LL records from 30 different software projects has been used for validation. Top-k and MAP, well-known accuracy metrics, have been used as well. The study results confirm the effectiveness of the automatic LL recall solution by a discerning accuracy of about 70%, which was increased to 74% in the case of hybridization. This eliminates the effort needed to manually search the LL repository, which positively encourages project managers to reuse the available LL knowledge – which in turn avoids old pitfalls and unleash hidden business opportunities

Process Mining Concepts for Discovering User Behavioral Patterns in Instrumented Software

Process Mining is a technique for discovering “in-use” processes from traces emitted to event logs. Researchers have recently explored applying this technique to documenting processes discovered in software applications. However, the requirements for emitting events to support Process Mining against software applications have not been well documented. Furthermore, the linking of end-user intentional behavior to software quality as demonstrated in the discovered processes has not been well articulated. After evaluating the literature, this thesis suggested focusing on user goals and actual, in-use processes as an input to an Agile software development life cycle in order to improve software quality. It also provided suggestions for instrumenting software applications to support Process Mining techniques