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Replications play a key role in Empirical Software Engineering by allowing the community to build knowledge about which results or observations hold under which conditions. Therefore, not only can a replication that produces similar results as the original experiment be viewed as successful, but a replication that produce results different from those of the original experiment can also be viewed as successful. In this paper we identify two types of replications: exact replications, in which the procedures of an experiment are followed as closely as possible; and conceptual replications, in which the same research question is evaluated by using a different experimental procedure. The focus of this paper is on exact replications. We further explore them to identify two sub-categories: dependent replications, where researchers attempt to keep all the conditions of the experiment the same or very similar and independent replications, where researchers deliberately vary one or more major aspects of the conditions of the experiment. We then discuss the role played by each type of replication in terms of its goals, benefits, and limitations. Finally, we highlight the importance of producing adequate documentation for an experiment (original or replication) to allow for replication. A properly documented replication provides the details necessary to gain a sufficient understanding of the study being replicated without requiring the replicator to slavishly follow the given procedures.Empir Software Eng (

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Abstract. Mature knowledge allows engineering disciplines the achievement of predictable results. Unfortunately, the type of knowledge used in software engineering can be considered to be of a relatively low maturity, and developers are guided by intuition, fashion or market-speak rather than by facts or undisputed statements proper to an engineering discipline. Testing techniques determine different criteria for selecting the test cases that will be used as input to the system under examination, which means that an effective and efficient selection of test cases conditions the success of the tests. The knowledge for selecting testing techniques should come from studies that empirically justify the benefits and application conditions of the different techniques. This paper analyzes the maturity level of the knowledge about testing techniques by examining existing empirical studies about these techniques. We have analyzed their results, and obtained a testing technique knowledge classification based on their factuality and objectivity, according to four parameters.

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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.

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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. Objectives: Identifying the techniques used to aggregate experiments' results within families in SE. Raising awareness of the importance of applying suitable aggregation techniques to reach reliable conclusions within families. Method: We conduct a systematic mapping study (SMS) to identify the aggregation techniques used to analyze families of experiments in SE. We outline the advantages and disadvantages of each aggregation technique according to mature experimental disciplines such as medicine and pharmacology. We provide preliminary recommendations to analyze and report families of experiments in view of families' common limitations with regard to joint data analysis. Results: Several aggregation techniques have been used to analyze SE families of experiments, including Narrative synthesis, Aggregated Data (AD), Individual Participant Data (IPD) mega-trial or stratified, and Aggregation of p-values. The rationale used to select aggregation techniques is rarely discussed within families. Families of experiments are commonly analyzed with unsuitable aggregation techniques according to the literature of mature experimental disciplines. Conclusion: Data analysis' reporting practices should be improved to increase the reliability and transparency of joint results. AD and IPD stratified appear to be suitable to analyze SE families of experiments.