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Responding to change over following a plan is one of the Agile Manifesto’s values. According to its original perspective, development teams should embrace changes due to a better understanding of the business domain or about the users’ needs. This kind of change is well understood and explored by practitioners and researchers. However, the issue of how development teams embrace changes due to unexpected, severe, and atypical external events (like the ones we are facing during COVID-19) have not been fully discussed. In this paper, we present our experience in managing the expectations and changing the development methods to mitigate the risks of carrying out a research and development project (named Virtual Social Office-VSO) during a pandemic time. To this end, we report on an observational study that explains the main differences between the procedures we expected to follow before the pandemic and the procedures we are following. We also present the percep- tions of both stakeholder and development teams about the impact of COVID-19 on VSO development. Although the development team quickly adapted itself to work from home, we observed some impact on the development plans. In particular, trying to reconcile the trade-offs between completeness and the tight schedule for the first release was a challenge, since we depended on different partners to collect the information that we need to make available in a mobile application (app) that corresponds to one of the VSO project outcomes. According to one stakeholder, the initial effort to design and prototype the VSO app (using the Design Thinking approach) before the start of the pandemic, has been considered essential to mitigate part of the threats that arose due to the COVID-19. We discuss part of these threats in this paper and the actions we have been taking to mitigate them. 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Background: The Java programming language version eight introduced several features that encourage the functional style of programming, including the support for lambda expressions and the Stream API. Currently, there is common wisdom that refactoring legacy code to introduce lambda expressions, besides other potential benefits, simplifies the code and improves program comprehension. Aims: The purpose of this work is to investigate this belief, conducting an in-depth study to evaluate the effect of introducing lambda expressions on program comprehension. Method: We conduct this research using a mixed-method study. For the quantitative method, we quantitatively analyze 166 pairs of code snippets extracted directly either from GitHub or from recommendations from three tools (RJTL, NetBeans, and IntelliJ). We also surveyed practitioners to collect their perceptions about the benefits on program comprehension when introducing lambda expressions. We asked practitioners to evaluate and rate sets of pairs of code snippets. Results: We found contradictory results in our research. Based on the quantitative assessment, we could not find evidence that the introduction of lambda expressions improves software readability—one of the components of program comprehension. Our results suggest that the transformations recommended by the aforementioned tools decrease program comprehension when assessed by two state-of-the-art models to estimate readability. Differently, our findings of the qualitative assessment suggest that the introduction of lambda expression improves program comprehension in three scenarios: when we convert anonymous inner classes to a lambda expression, structural loops with inner conditional to a anyMatch operator, and structural loops to filter operator combined with a collect method. Implications: We argue in this paper that one can improve program comprehension when she applies particular transformations to introduce lambda expressions (e.g., replacing anonymous inner classes with lambda expressions). Also, the opinion of the participants shines the opportunities in which a transformation for introducing lambda might be advantageous. This might support the implementation of effective tools for automatic program transformations.