Understanding and effectively managing Technical Debt (TD) remains a vital challenge in software engineering. While many studies on code-level TD have been published, few illustrate the business impact of low-quality source code. In this study, we combine two publicly available datasets to study the association between code quality on the one hand, and defect count and implementation time on the other hand. We introduce a value-creation model, derived from regression analyses, to explore relative changes from a baseline. Our results show that the associations vary across different intervals of code quality. Furthermore, the value model suggests strong non-linearities at the extremes of the code quality spectrum. Most importantly, the model suggests amplified returns on investment in the upper end. We discuss the findings within the context of the "broken windows" theory and recommend organizations to diligently prevent the introduction of code smells in files with high churn. Finally, we argue that the value-creation model can be used to initiate discussions regarding the return on investment in refactoring efforts.

[Context] Accurate time estimation is a critical aspect of predictable software engineering. Previous work shows that low source code quality increases the uncertainty in issue resolution times. [Objective] Our goal is to evaluate how developers’ project experience and file ownership are related to issue resolution times. [Method] We mine 40 proprietary software repositories and conduct an observational study. Using CodeScene, we measure source code quality and active development time connected to Jira issues. [Results] Most source code changes are made by either a marginal or dominant code owner. Also, most changes to low-quality source code are made by developers with low levels of ownership. In low-quality source code, marginal owners need 45% more time for small changes, and 93% more time for large changes. [Conclusions] Collective code ownership is a popular target, but industry practice results in many dominant and marginal owners. Marginal owners are particularly hampered when working with low-quality source code, which leads to productivity losses. In codebases plagued by technical debt, newly onboarded developers will require more time to complete tasks.

Hierarchical networks are prevalent in nature and society, corresponding to groups of actors - animals, humans or even robots - organized according to a pyramidal structure with decision makers at the top and followers at the bottom. While this phenomenon is seemingly universal, the underlying governing principles are poorly understood. Here we study the emergence of hierarchies in groups of people playing a simple dot guessing game in controlled experiments, lasting for about 40 rounds, conducted over the Internet. During the games, the players had the possibility to look at the answer of a limited number of other players of their choice. This act of asking for advice defines a directed connection between the involved players, and according to our analysis, the initial random configuration of the emerging networks became more structured over time, showing signs of hierarchy towards the end of the game. In addition, the achieved score of the players appeared to be correlated with their position in the hierarchy. These results indicate that under certain conditions imitation and limited knowledge about the performance of other actors is sufficient for the emergence of hierarchy in a social group.

Outside Africa, the global phylogeography of HIV is characterized by compartmentalized local epidemics that are typically dominated by a single subtype, which indicates strong founder effects. We hypothesized that the competition of viral strains at the epidemic level may involve an advantage of the resident strain that was the first to colonize a population. Such an effect would slow down the invasion of new strains, and thus also the diversification of the epidemic. We developed a stochastic modelling framework to simulate HIV epidemics over dynamic contact networks. We simulated epidemics in which the second strain was introduced into a population where the first strain had established a steady-state epidemic, and assessed whether, and on what time scale, the second strain was able to spread in the population. Simulations were parameterized based on empirical data; we tested scenarios with varying levels of overall prevalence. The spread of the second strain occurred on a much slower time scale compared with the initial expansion of the first strain. With strains of equal transmission efficiency, the second strain was unable to invade on a time scale relevant for the history of the HIV pandemic. To become dominant over a time scale of decades, the second strain needed considerable (>25%) advantage in transmission efficiency over the resident strain. The inhibition effect was weaker if the second strain was introduced while the first strain was still in its growth phase. We also tested how possible mechanisms of interference (inhibition of superinfection, depletion of highly connected hubs in the network, one-time acute peak of infectiousness) contribute to the inhibition effect. Our simulations confirmed a strong first comer advantage in the competition dynamics of HIV at the population level, which may explain the global phylogeography of the virus and may influence the future evolution of the pandemic.