Software evolves continuously. Its behavior must be validated by engineers who perform daily development and maintenance tasks. However, despite its high importance, information needs for validating evolving software has not been systematically studied in an industrial setting. Such lack of empirical knowledge hinders attempts to understand this fundamental practice and improve the corresponding tool support. This paper presents a study on 194 Site Reliability Engineers (SREs) at Google to explore their information needs. The results suggest several directions where software engineering researchers may consider putting effort to develop new techniques and tool support that matter to practitioners.

End-to-end latency of serving jobs in distributed and shared environments, such as a Cloud, is an important metric for jobs' owners and infrastructure providers. Yet it is notoriously challenging to model precisely, since it is affected by a large collection of unrelated moving pieces, from the software design to the job schedulers strategies. In this work we present a novel approach to modeling latency, by tracking how it varies with CPU usage. We train a classifier to automatically assign the latency behavior of methods in three classes: constant latency regardless of CPU, uncorrelated latency and CPU, and predictable latency as a function of CPU. We use our model on a random sample of serving jobs running on the Google infrastructure. We illustrate unexpected and insightful patterns of latency variations with CPU. The visualization of latency-CPU variations and the corresponding class may be used by both jobs' owners and infrastructure providers, for a variety of applications, such as smarter latency alerting, latency-aware configuration of jobs, and automated detection of changes in behavior, either over time, during pre-release testing, or across data centers.

In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim "gaits" in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes "graceful" from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible.

We present a segmentation method for live cell images, using graph cuts and learning methods. The images used here are particularly challenging because of the shared grey-level distributions of cells and background, which only differ by their textures, and the local imprecision around cell borders. We use the PnPotts model recently presented by Kohli et al. [9]: functions on higher-order cliques of pixels are included into the traditional Potts model, allowing us to account for local texture features, and to find the optimal solution efficiently. We use learning methods to define the potential functions used in the PnPotts model. We present the model and the learning methods we used, and compare our segmentation results with similar work in cytometry. While our method performs similarly, it requires little manual tuning and thus is straightforward to adapt to other images.