Design review is an important initial phase of the software development life-cycle where stakeholders gain and discuss early insights into the design's viability, discover potentially costly mistakes, and identify inconsistencies and inadequacies. For improved development velocity, it is important that design owners get their designs approved as Quickly as possible. In this paper, we discuss how engineering design reviews are typically conducted at Google, and propose a novel, structured, automated solution to improve design review velocity. Based on data collected on 141,652 approved documents authored by 41,030 users over four years, we show that our proposed solution decreases median time-to-approval by 25%, and provides further gains when used consistently. We also provide qualitative data to demonstrate our solution's success, discuss factors that impact design review latency, propose strategies to tackle them, and share lessons learned from the usage of our solution.

Regression testing is a critical part of software development and maintenance. It ensures that modifications to existing software do not break existing behavior and functionality.One of the key assumptions about regression tests is that their results are deterministic: when executed without any modifications with the same configuration, either they always fail or they always pass. In practice, however, there exist tests that are non-deterministic, called flaky tests. Flaky tests cause the results of test runs to be unreliable, and they disrupt the software development workflow. In this paper, we present a novel technique to automatically identify the locations of the root causes of flaky tests on the code level to help developers debug and fix them. We study the technique on flaky tests across 428 projects at Google. Based on our case studies, the technique helps identify the location of the root causes of flakiness with 82% accuracy. Furthermore, our studies show that integration into the appropriate developer workflows, simplicity of debugging aides and fully automated fixes are crucial and preferred components for adoption and usability of flakiness debugging and fixing tools.

Requirements traceability is linking requirements to software artifacts, such as source code, test-cases and configuration files. For stakeholders of software, it is important to understand which requirements were tested, whether sufficiently, if at all. Hence tracing requirements in test-cases is an important problem. In this paper, we build on existing research and use features, realization of functional requirements in software [15], to automatically create requirements traceability links between requirements and test-cases. We evaluate our approach on a chat system, Apache Pool [21] and Apache Log4j [11]. We obtain precision/recall levels of more than 90%, an improvement upon currently existing Information Retrieval approaches when tested on the same case studies.

Currently an abundance of historical manuscripts, journals, and scientific notes remain largely unaccessible in library archives. Manual transcription and publication of such documents is unlikely, and automatic transcription with high enough accuracy to support a traditional text search is difficult. In this work we describe a lexicon-free system for performing text queries on off-line printed and handwritten Arabic documents. Our segmentation-based approach utilizes gHMMs with a bigram letter transition model, and KPCA/LDA for letter discrimination. The segmentation stage is integrated with inference. We show that our method is robust to varying letter forms, ligatures, and overlaps. Additionally, we find that ignoring letters beyond the adjoining neighbors has little effect on inference and localization, which leads to a significant performance increase over standard dynamic programming. Finally, we discuss an extension to perform batch searches of large word lists for indexing purposes.