Satisfactory software performance is essential for the adoption and the success of a product. In organizations that follow traditional software development models (e.g., waterfall), Software Performance Engineering (SPE) involves time-consuming experimental modeling and performance testing outside the actual production environment. Such existing SPE methods, however, are not optimized for environments utilizing Continuous Integration (CI) and Continuous Delivery (CD) that result in high frequency and high volume of code changes. We present a summary of lessons learned and propose improvements to the SPE process in the context of CI/CD. Our findings are based on SPE work on two products conducted over 5 years at a major online services company. We find that (a) SPE has mainly become a post hoc activity based on data from the production environment, (b) successful application of SPE techniques require frequent re-evaluation of priorities, and (c) engineers working on SPE require a broader skill set than one traditionally possessed by engineers working on performance. CCS CONCEPTS• Software and its engineering → Software post-development issues; Software performance; Programming teams.

Using custom memory allocators is an efficient performance optimization technique. However, dependency on a custom allocator can introduce several maintenance-related issues. We present lessons learned from the industry and provide critical guidance for using custom memory allocators and enumerate various challenges associated with integrating them. These recommendations are based on years of experience incorporating custom allocators into different industrial software projects.

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Studies on software evolution explore code churn and code velocity at the abstraction level of a company or an entire project. We argue that this approach misses the differences among abstractions layers and subsystems of large projects. We conduct a case study on four BSD family operating systems: DragonFlyBSD, FreeBSD, NetBSD, and OpenBSD, to investigate the evolution of code churn and code velocity across kernel and non-kernel code. We mine commits for characteristics such as annual growth rate, commit types, change type ratio, and size taxonomy, indicating code churn. Likewise, we investigate code velocity in terms of code review periods, i.e., time-to-firstresponse, time-to-accept, and time-to-merge.Our study provides evidence that software evolves differently at abstraction layers: kernel and non-kernel. The study finds similarities in the code base growth rate and distribution of commit types (neutral, additive, and subtractive) across BSD subsystems, however, (a) most commits contain either kernel or non-kernel code, (b) kernel commits are larger than non-kernel commits, and (c) code reviews for kernel code take longer than non-kernel code.