Abstract This paper proposes a new methodology for early validation of high-level requirements on cyber-physical systems with the aim of improving their quality and, thus, lowering chances of specification errors propagating into later stages of development where it is much more expensive to fix them. The paper presents a transformation of a real-world requirements specification of a medical device—the Patient-Controlled Analgesia (PCA) Pump—into an Event Calculus model that is then evaluated using Answer Set Programming and the s(CASP) system. The evaluation under s(CASP) allowed deductive as well as abductive reasoning about the specified functionality of the PCA pump on the conceptual level with minimal implementation or design dependent influences and led to fully automatically detected nuanced violations of critical safety properties. Further, the paper discusses scalability and non-termination challenges that had to be faced in the evaluation and techniques proposed to (partially) solve them. Finally, ideas for improving s(CASP) to overcome its evaluation limitations that still persist as well as to increase its expressiveness are presented.

This paper presents GraphJet, a new graph-based system for generating content recommendations at Twitter. As motivation, we trace the evolution of our formulation and approach to the graph recommendation problem, embodied in successive generations of systems. Two trends can be identified: supplementing batch with real-time processing and a broadening of the scope of recommendations from users to content. Both of these trends come together in Graph-Jet, an in-memory graph processing engine that maintains a real-time bipartite interaction graph between users and tweets. The storage engine implements a simple API, but one that is sufficiently expressive to support a range of recommendation algorithms based on random walks that we have refined over the years. Similar to Cassovary, a previous graph recommendation engine developed at Twitter, GraphJet assumes that the entire graph can be held in memory on a single server. The system organizes the interaction graph into temporally-partitioned index segments that hold adjacency lists. GraphJet is able to support rapid ingestion of edges while concurrently serving lookup queries through a combination of compact edge encoding and a dynamic memory allocation scheme that exploits power-law characteristics of the graph. Each GraphJet server ingests up to one million graph edges per second, and in steady state, computes up to 500 recommendations per second, which translates into several million edge read operations per second.

The web today is increasingly characterized by social and real-time signals, which we believe represent two frontiers in information retrieval. In this paper, we present Early bird, the core retrieval engine that powers Twitter's real-time search service. Although Early bird builds and maintains inverted indexes like nearly all modern retrieval engines, its index structures differ from those built to support traditional web search. We describe these differences and present the rationale behind our design. A key requirement of real-time search is the ability to ingest content rapidly and make it searchable immediately, while concurrently supporting low-latency, high-throughput query evaluation. These demands are met with a single-writer, multiple-reader concurrency model and the targeted use of memory barriers. Early bird represents a point in the design space of real-time search engines that has worked well for Twitter's needs. By sharing our experiences, we hope to spur additional interest and innovation in this exciting space.

In this paper we introduce a new load testing technique called Deterministic Markov State Testing and report on its application. Our approach is called “deterministic” because the sequence of test case execution is set at planning time, and “state testing” because each test case certifies a unique software state. There are four main advantages of Deterministic Markov State Testing for system testers: provision of precise software state information for root cause analysis in load test, accommodation for limitations of the system test lab configuration, higher acceleration ratios in system test, and simple management of distributed execution of test cases. System testers using the proposed method have great flexibility in dealing with common system test problems: limited access to the system test environment, unstable software, or changing operational conditions. Because each test case verifies correct execution on a path from the idle state to the software state under test, our method does not require the continuous execution of all test cases. Deterministic Markov State Testing is operational-profile-based, and allows for measurement of software reliability robustness when the operational profile changes.