Context: Replication plays an important role in experimental disciplines. There are still many uncertain- ties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experiment- ers, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? Objective: To improve our understanding of SE experiment replication, in this work we propose a classi- fication which is intend to provide experimenters with guidance about what types of replication they can perform. Method: The research approach followed is structured according to the following activities: (1) a litera- ture review of experiment replication in SE and in other disciplines, (2) identification of typical elements that compose an experimental configuration, (3) identification of different replications purposes and (4) development of a classification of experiment replications for SE. Results: We propose a classification of replications which provides experimenters in SE with guidance about what changes can they make in a replication and, based on these, what verification purposes such a replication can serve. The proposed classification helped to accommodate opposing views within a broader framework, it is capable of accounting for less similar replications to more similar ones regarding the baseline experiment. Conclusion: The aim of replication is to verify results, but different types of replication serve special ver- ification purposes and afford different degrees of change. Each replication type helps to discover partic- ular experimental conditions that might influence the results. The proposed classification can be used to identify changes in a replication and, based on these, understand the level of verification

Software regression testing techniques verify previous functionalities each time software modifications occur or new characteristics are added. With the aim of gaining a better understanding of this subject, in this work we present a survey of software regression testing techniques applied in the last 15 years; taking into account its application domain, kind of metrics they use, its application strategies and the phase of the software development process where they are applied. From an outcome of 460 papers, a set of 25 papers describing the use of 31 software testing regression techniques were identified. Results of this survey suggest that at the time of applying a regression testing techniques, metrics like cost and fault detection efficiency are the most relevant. Most of the techniques were assessed with instrumented programs (experimental cases) under academic settings. Conversely, we observe a minimum set of software regression techniques applied in industrial settings, mainly, under corrective and maintenance approaches. Finally, we observe a trend using some regression techniques under agile approaches.

Context: Families of experiments (i.e., groups of experiments with the same goal) are on the rise in Software Engineering (SE). Selecting unsuitable aggregation techniques to analyze families may undermine their potential to provide in-depth insights from experiments' results.

Software regression testing verifies previous features on a software product when it is modified or new features are added to it. Because of the nature of regression testing it is a costly process. Different approaches have been proposed to reduce the costs of this activity, among which are: minimization, prioritization, and selection of test cases. Recently, soft computing techniques, such as data mining, machine learning, and others have been used to make regression testing more efficient and effective. Currently, in different contexts, to a greater or lesser extent, software products have access to databases (DBs). Given this situation, it is necessary to consider regression testing also for software products such as information systems that are usually integrated with or connected to DBs. In this paper, we present a selection regression testing approach that utilizes a combination of unsupervised clustering with random values, unit tests, and the DB schema to determine the test cases related to modifications or new features added to software products connected to DBs. Our proposed approach is empirically evaluated with two database software applications in a production context. Effectiveness metrics, such as test suite reduction, fault detection capability, recall, precision, and the F-measure are examined. Our results suggest that the proposed approach is enough effective with the resulting clusters of test cases.