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Context: Empirical studies are gaining recognition in the Software Engineering (SE) research community. In order to foster empirical research, it is essential understand the environments, guidelines, process, and other mechanisms available to support these studies in SE. Goal: Identifying the mechanisms used to support the empirical strategies adopted by the researches in the major Empirical Software Engineering (ESE) scientific venues. Method: We performed a systematic mapping study that included all full papers published at EASE, ESEM and ESEJ since their first editions. A total of 898 studies were selected. Results: We provide the full list of identified support mechanisms and the strategies that uses them. The most commonly mechanisms used to support the empirical strategies were two sets of guidelines, one to secondary studies and another to experiments. The most reported empirical strategies are experiments and case studies. Conclusions: The use of empirical methods in SE has increased over the years but many studies do not apply these methods nor use mechanisms to guide their research. Therefore, the list of support mechanisms, where and how they were applied is a major asset to the SE community. Such asset can foster empirical studies aiding the choice regarding which strategies and mechanisms to use in a research. Also, we identified new perspectives and gaps that foster the development of resources to aid empirical studies.

Background] Nowadays, there is a massive growth of data volume and speed in many types of systems. It introduces new needs for infrastructure and applications that have to handle streams of data with low latency and high throughput. Testing applications that process such data streams has become a significant challenge for engineers. Companies are adopting different approaches to dealing with this issue. Some have developed their own solutions for testing, while others have adopted a combination of existing testing techniques. There is no consensus about how or in which contexts such solutions can be implemented.[Aims] To the best of our knowledge, there is no consolidated literature on that topic. The present paper is an attempt to fill this gap by conducting an exploratory study with practitioners. [Method] We used qualitative methods in this research, in particular interviews and survey. We interviewed 12 professionals who work in projects related to data streams, and also administered a questionnaire with other 105 professionals. The interviews went through a transcription and coding process, and the questionnaires were analysed to reinforce findings.[Results] This study presents current practices around software testing in data stream processing applications. These practices involve methodologies, techniques, and tools. [Conclusions] Our main contribution is a compendium of alternatives for many of the challenges that arise when testing streaming applications from a state-of-the-practice perspective.

In recent years, the use and investigations about Grey Literature (GL) increased, in particular, in Software Engineering (SE) research. However, its understanding is still scarce and sometimes controversial, such as interpreting GL types and assessing their credibility. This study aimed to understand the credibility aspects that SE researchers consider in assessing GL and its types. To achieve this goal, we surveyed 53 SE researchers (who answered that they have used GL in our previous investigation), receiving a total of 34 valid responses. Our main findings show that: 1) GL source produced or cited by a renowned source is the main credibility criteria used to assess GL, 2) most of the GL types tend to have a Low to Moderate level of Control and Expertise, 3) there is a positive statistical correlation between the level of Control and Expertise for most GL types, and 4) the different respondent profiles shared similar opinions about the credibility criteria. Our investigation contributes to helping future SE researchers that intend to use GL with more credibility. Additionally, shows the need for future studies to better understand the GL types in SE research.