Rana Alkadhi

Paul Ralph, Sebastian Baltes, Gianisa Adisaputri et al.

Context. As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective. This study investigates the effects of the pandemic on developers' wellbeing and productivity. Method. A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results. The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers' wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions. To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees' home offices. Women, parents and disabled persons may require extra support.

Rana Alkadhi, Teodora Lata, Emitza Guzman et al.

Chat messages of development teams play an increasingly significant role in software development, having replaced emails in some cases. Chat messages contain information about discussed issues, considered alternatives and argumentation leading to the decisions made during software development. These elements, defined as rationale, are invaluable during software evolution for documenting and reusing development knowledge. Rationale is also essential for coping with changes and for effective maintenance of the software system. However, exploiting the rationale hidden in the chat messages is challenging due to the high volume of unstructured messages covering a wide range of topics. This work presents the results of an exploratory study examining the frequency of rationale in chat messages, the completeness of the available rationale and the potential of automatic techniques for rationale extraction. For this purpose, we apply content analysis and machine learning techniques on more than 8,700 chat messages from three software development projects. Our results show that chat messages are a rich source of rationale and that machine learning is a promising technique for detecting rationale and identifying different rationale elements.