With the COVID-19 pandemic, Scrum teams had to switch abruptly from a traditional working setting into an enforced working from home one. This abrupt switch had an impact on software projects. Thus, it is necessary to understand how potential future disruptive events will impact Agile software teams' ability to deliver successful projects while working from home. To investigate this problem, we used a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through semi-structured interviews. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares-Structural Equation Modeling (PLS-SEM) surveying 138 software engineers who worked from home within Scrum projects. We concluded that all the latent variables identified in our model are reliable, and all the hypotheses are significant. This paper emphasizes the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the ability of working from home and the use of Scrum both contribute to project success, with Scrum acting as a mediator.

The COVID-19 outbreak has admittedly caused interruptions to production, transportation, and mobility, therefore, having a significant impact on the global supply and demand chain's well-functioning. But what happened to companies developing digital services, such as software? How has the enforced Working-From-Home (WFH) mode impacted their ability to deliver software, if at all? This article shares our findings from monitoring the WFH during 2020 in an international software company with engineers located in Sweden, the USA, and the UK. We analyzed different aspects of productivity, such as developer job satisfaction and well-being, activity, communication and collaboration, efficiency and flow based on the archives of commit data, calendar invites, Slack communication, the internal reports of WFH experiences, and 30 interviews carried out in April/May and September 2020. We add more objective evidence to the existing COVID-19 studies the vast majority of which are based on self-reported productivity from the early months of the pandemic. We find that engineers continue committing code and carrying out their daily duties, as their routines adjust to "the new norm". Our key message is that software engineers can work from home and quickly adjust their tactical approaches to the changes of unprecedented scale. Further, WFH has its benefits, including better work-life balance, improved flow, and improved quality of distributed meetings and events. Yet, WFH is not challenge free: not everybody feels equally productive working from home, work hours for many increased, while physical activity, socialization, pairing and opportunities to connect to unfamiliar colleagues decreased. Information sharing and meeting patterns also changed. Finally, experiences gained during the pandemic will have a lasting impact on the future of the workplace. The results of an internal company-wide survey suggest that only 9% of engineers will return to work in the office full time. Our article concludes with the InterSoft's strategy for work from anywhere (WFX), and a list of useful adjustments for a better WFH.

In early 2020, the Covid-19 pandemic forced employees in tech companies worldwide to abruptly transition from working in offices to working from their homes. During two years of predominantly working from home, employees and managers alike formed expectations about what post-pandemic working life should look like. Many companies are experimenting with new work policies that balance employee- and manager expectations regarding where, when and how work should be done in the future. In this article, we gather experiences of the new trend of remote working based on the synthesis of 22 company-internal surveys of employee preferences for WFH, and 26 post-pandemic work policies from 17 companies and their sites, covering 12 countries in total. Our results are threefold. First, through the new work policies, all companies formally give employees more flexibility regarding working time and location. Second, there is a great variation in how much flexibility the companies are willing to yield to the employees. The paper details the different formulations that companies adopted to document the extent of permitted WFH, exceptions, relocation permits and the authorisation procedures. Third, we document a change in the psychological contract between employees and managers, where the option of working from home is converted from an exclusive perk that managers could choose to give to the few, to a core privilege that all employees feel they are entitled to. Finally, there are indications that as the companies learn and solicit feedback regarding the efficiency of the chosen strategies, we will see further developments and changes in the work policies concerning how much flexibility to work whenever and from wherever they grant. Through these findings, the paper contributes to a growing literature about the new trends emerging from the pandemic in tech companies and spells out practical implications onwards.

With the COVID-19 pandemic, strict measures have been taken to slow down the spread of the virus, and consequently, software professionals have been forced to work from home. However, home-based working entails many challenges, as the home environment is shared by the whole family simultaneously under pandemic conditions. The aim of this study is to explore software professionals' mental well-being and work engagement and the relationships of these variables with job strain and resource-related factors in the forced home-based work setting during the COVID-19 pandemic. An online cross-sectional survey based on primarily well-known, validated scales was conducted with software professionals in Turkey. The analysis of the results was performed through hierarchical multivariate regression. The results suggest that despite the negative effect of job strain, the resource-related protective factors, namely, sleep quality, decision latitude, work-life balance, exercise predict mental well-being. Additionally, work engagement is predicted by job strain, sleep quality, and decision latitude. The results of the study will provide valuable insights to management of the software companies and professionals about the precautions that can be taken to have a better home-based working experience such as allowing greater autonomy and enhancing the quality of sleep and hence mitigating the negative effects of pandemic emergency situations on software professionals' mental well-being and work engagement.

The COVID-19 pandemic triggered a natural experiment of an unprecedented scale as companies closed their offices and sent employees to work from home. Many managers were concerned that their engineers would not be able to work effectively from home, or lack the motivation to do so, and that they would lose control and not even notice when things go wrong. As many companies announced their post-COVID permanent remote-work or hybrid home/office policies, the question of what can be expected from software engineers who work from home becomes more and more relevant.

More than 78 countries have developed COVID contact-tracing apps to limit the spread of coronavirus. However, many experts and scientists cast doubt on the effectiveness of those apps. For each app, a large number of reviews have been entered by end-users in app stores.

The COVID-19 outbreak, also known as the coronavirus pandemic, has left its mark on every aspect of our lives and at the time of this writing is still an ongoing battle. Beyond the immediate global-wide health response, the pandemic has triggered a significant number of IT initiatives to track, visualize, analyze and potentially mitigate the phenomenon. For individuals or organizations interested in developing COVID-19 related software, knowledge-sharing communities such as Stack Overflow proved to be an effective source of information for tackling commonly encountered problems. As an additional contribution to the investigation of this unprecedented health crisis and to assess how fast and how well the community of developers has responded, we performed a study on COVID-19 related posts in Stack Overflow. In particular, we profiled relevant questions based on key post features and their evolution, identified the most prominent technologies adopted for developing COVID-19 software and their interrelations and focused on the most persevering problems faced by developers. For the analysis of posts we employed descriptive statistics, Association Rule Graphs, Survival Analysis and Latent Dirichlet Allocation. The results reveal that the response of the developers' community to the pandemic was immediate and that the interest of developers on COVID-19 related challenges was sustained after its initial peak. In terms of the problems addressed, the results show a clear focus on COVID-19 data collection, analysis and visualization from/to the web, in line with the general needs for monitoring the pandemic.

As every software artifact, also software models are subject to continuous evolution. The operations applied between two successive versions of a model are crucial for understanding its evolution. Generic approaches for detecting operations a posteriori identify , but neglect , such as refactorings, which leads to cluttered difference reports. To tackle this limitation, we present an orthogonal extension of existing atomic operation detection approaches for detecting also composite operations. Our approach searches for occurrences of composite operations within a set of detected atomic operations in a post-processing manner. One major benefit is the reuse of specifications available for executing composite operations also for detecting applications of them. We evaluate the accuracy of the approach in a real-world case study and investigate the scalability of our implementation in an experiment.

Traceability relations support stakeholders in understanding the dependencies between artifacts created during the development of a software system and thus enable many development-related tasks. To ensure that the anticipated benefits of these tasks can be realized, it is necessary to have an up-to-date set of traceability relations between the established artifacts. This goal requires the creation of traceability relations during the initial development process. Furthermore, the goal also requires the maintenance of traceability relations over time as the software system evolves in order to prevent their decay. In this paper, an approach is discussed that supports the (semi-) automated update of traceability relations between requirements, analysis and design models of software systems expressed in the UML. This is made possible by analyzing change events that have been captured while working within a third-party UML modeling tool. Within the captured flow of events, development activities comprised of several events are recognized. These are matched with predefined rules that direct the update of impacted traceability relations. The overall approach is supported by a prototype tool and empirical results on the effectiveness of tool-supported traceability maintenance are provided.

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no "test oracle" to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique "metamorphic testing", which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program.