Sira Vegas’s research while affiliated with Universidad Politécnica de Madrid and other places

Experiments are a commonly used method of research in software engineering (SE). Researchers report their experiments following detailed guidelines. However, researchers do not, in the field of test-driven development (TDD) at least, specify how they operationalized the response variables and, particularly, the measurement process. This article has three aims: (i) identify the response variable operationalization components in TDD experiments that study external quality; (ii) study their influence on the experimental results; (iii) determine if the experiment reports describe the measurement process components that have an impact on the results. We used two-part sequential mixed methods research. The first part of the research adopts a quantitative approach applying a statistical analysis of the impact of the operationalization components on the experimental results. The second part follows on with a qualitative approach applying a systematic mapping study (SMS). The test suites, intervention types and measurers have an influence on the measurements and results of the statistical analysis of TDD experiments in SE. The test suites have a major impact on both the measurements and the results of the experiments. The intervention type has less impact on the results than on the measurements. While the measurers have an impact on the measurements, this is not transferred to the experimental results. On the other hand, the results of our SMS confirm that TDD experiments do not usually report either the test suites, the test case generation method, or the details of how external quality was measured. A measurement protocol should be used to assure that the measurements made by different measurers are similar. It is necessary to report the test cases, the experimental task and the intervention type in order to be able to reproduce the measurements and statistical analyses, as well as to replicate experiments and build dependable families of experiments.

Experimentation is an essential method for causal inference in any empirical discipline. Crossover-design experiments are common in Software Engineering (SE) research. In these, subjects apply more than one treatment in different orders. This design increases the amount of obtained data and deals with subject variability but introduces threats to internal validity like the learning and carryover effect. Vegas et al. reviewed the state of practice for crossover designs in SE research and provided guidelines on how to address its threats during data analysis while still harnessing its benefits. In this paper, we reflect on the impact of these guidelines and review the state of analysis of crossover design experiments in SE publications between 2015 and March 2024. To this end, by conducting a forward snowballing of the guidelines, we survey 136 publications reporting 67 crossover-design experiments and evaluate their data analysis against the provided guidelines. The results show that the validity of data analyses has improved compared to the original state of analysis. Still, despite the explicit guidelines, only 29.5% of all threats to validity were addressed properly. While the maturation and the optimal sequence threats are properly addressed in 35.8% and 38.8% of all studies in our sample respectively, the carryover threat is only modeled in about 3% of the observed cases. The lack of adherence to the analysis guidelines threatens the validity of the conclusions drawn from crossover design experiments

Test-driven development (TDD) is a widely used agile practice. However, very little is known with certainty about TDD's underlying foundations, i.e., the way TDD works. In this paper, we propose a theoretical framework for TDD, with the following characteristics: 1) Each TDD cycle represents a vertical slice of a (probably also small) user story, 2) vertical slices are captured using contracts, implicit in the developers' minds, and 3) the code created during a TDD cycle is a sliced-based specification of a code oracle, using the contracts as slicing pre/post-conditions. We have checked the connections among TDD, contracts, and slices using a controlled experiment conducted in the industry.

Experiments are a commonly used method of research in software engineering (SE). Researchers report their experiments following detailed guidelines. However, researchers do not, in the field of test-driven development (TDD) at least, specify how they operationalized the response variables and the measurement process. This article has three aims: (i) identify the response variable operationalization components in TDD experiments that study external quality; (ii) study their influence on the experimental results;(ii) determine if the experiment reports describe the measurement process components that have an impact on the results. Sequential mixed method. The first part of the research adopts a quantitative approach applying a statistical an\'alisis (SA) of the impact of the operationalization components on the experimental results. The second part follows on with a qualitative approach applying a systematic mapping study (SMS). The test suites, intervention types and measurers have an influence on the measurements and results of the SA of TDD experiments in SE. The test suites have a major impact on both the measurements and the results of the experiments. The intervention type has less impact on the results than on the measurements. While the measurers have an impact on the measurements, this is not transferred to the experimental results. On the other hand, the results of our SMS confirm that TDD experiments do not usually report either the test suites, the test case generation method, or the details of how external quality was measured. A measurement protocol should be used to assure that the measurements made by different measurers are similar. It is necessary to report the test cases, the experimental task and the intervention type in order to be able to reproduce the measurements and SA, as well as to replicate experiments and build dependable families of experiments.

[Context] Microservices enable the decomposition of applications into small and independent services connected together. The independence between services could positively affect the development velocity of a project, which is considered an important metric measuring the time taken to implement features and fix bugs. However, no studies have investigated the connection between microservices and development velocity. [Objective and Method] The goal of this study plan is to investigate the effect microservices have on development velocity. The study compares GitHub projects adopting microservices from the beginning and similar projects using monolithic architectures. We designed this study using a cohort study method, to enable obtaining a high level of evidence. [Results] The result of this work enables the confirmation of the effective improvement of the development velocity of microservices. Moreover, this study will contribute to the body of knowledge of empirical methods being among the first works adopting the cohort study methodology.

Software engineering techniques are increasingly relying on deep learning approaches to support many software engineering tasks, from bug triaging to code generation. To assess the efficacy of such techniques researchers typically perform controlled experiments. Conducting these experiments, however, is particularly challenging given the complexity of the space of variables involved, from specialized and intricate architectures and algorithms to a large number of training hyper-parameters and choices of evolving datasets, all compounded by how rapidly the machine learning technology is advancing, and the inherent sources of randomness in the training process. In this work we conduct a mapping study, examining 194 experiments with techniques that rely on deep neural networks appearing in 55 papers published in premier software engineering venues to provide a characterization of the state-of-the-practice, pinpointing experiments common trends and pitfalls. Our study reveals that most of the experiments, including those that have received ACM artifact badges, have fundamental limitations that raise doubts about the reliability of their findings. More specifically, we find: weak analyses to determine that there is a true relationship between independent and dependent variables (87% of the experiments); limited control over the space of DNN relevant variables, which can render a relationship between dependent variables and treatments that may not be causal but rather correlational (100% of the experiments); and lack of specificity in terms of what are the DNN variables and their values utilized in the experiments (86% of the experiments) to define the treatments being applied, which makes it unclear whether the techniques designed are the ones being assessed, or how the sources of extraneous variation are controlled. We provide some practical recommendations to address these limitations.