4,222 research outputs found
Symbolic QED Pre-silicon Verification for Automotive Microcontroller Cores: Industrial Case Study
We present an industrial case study that demonstrates the practicality and
effectiveness of Symbolic Quick Error Detection (Symbolic QED) in detecting
logic design flaws (logic bugs) during pre-silicon verification. Our study
focuses on several microcontroller core designs (~1,800 flip-flops, ~70,000
logic gates) that have been extensively verified using an industrial
verification flow and used for various commercial automotive products. The
results of our study are as follows: 1. Symbolic QED detected all logic bugs in
the designs that were detected by the industrial verification flow (which
includes various flavors of simulation-based verification and formal
verification). 2. Symbolic QED detected additional logic bugs that were not
recorded as detected by the industrial verification flow. (These additional
bugs were also perhaps detected by the industrial verification flow.) 3.
Symbolic QED enables significant design productivity improvements: (a) 8X
improved (i.e., reduced) verification effort for a new design (8 person-weeks
for Symbolic QED vs. 17 person-months using the industrial verification flow).
(b) 60X improved verification effort for subsequent designs (2 person-days for
Symbolic QED vs. 4-7 person-months using the industrial verification flow). (c)
Quick bug detection (runtime of 20 seconds or less), together with short
counterexamples (10 or fewer instructions) for quick debug, using Symbolic QED
Data-Driven Application Maintenance: Views from the Trenches
In this paper we present our experience during design, development, and pilot
deployments of a data-driven machine learning based application maintenance
solution. We implemented a proof of concept to address a spectrum of
interrelated problems encountered in application maintenance projects including
duplicate incident ticket identification, assignee recommendation, theme
mining, and mapping of incidents to business processes. In the context of IT
services, these problems are frequently encountered, yet there is a gap in
bringing automation and optimization. Despite long-standing research around
mining and analysis of software repositories, such research outputs are not
adopted well in practice due to the constraints these solutions impose on the
users. We discuss need for designing pragmatic solutions with low barriers to
adoption and addressing right level of complexity of problems with respect to
underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th
International Workshop on Software Engineering Research and Industrial
Practice (SER&IP), IEEE Press, pp. 48-54, 201
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