An open framework for semantic code queries on heterogeneous repositories

To help developers understand and reuse programs, semantic queries on the source code itself is attractive. Although programs in heterogeneous languages are being controlled for collaborative software development, most queries supported by various source code repositories are based either on the metadata of the repositories, or on indexed identifiers and method signatures. Few provide full support to search for structures that are common across different programming languages and different viewpoints (hence heterogeneous). To facilitate understanding and reuses, in this paper, we propose a novel source code query framework that (1) transforms source code to a unified abstract syntax format, and handles heterogeneity (non-isomorphism) at the abstract syntax level; (2) stores source code on a cloud-based NoSQL storage in MongoDB; (3) rewrites semantic query patterns into the NoSQL form. The efficiency of the framework has been evaluated to support several open-source hosting platforms

Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.Comment: 6 pages, 2 figures, 5 tables, accepted by DAC'2

Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models

peer reviewe

Involvement of Abscisic Acid in PSII Photodamage and D1 Protein Turnover for Light-Induced Premature Senescence of Rice Flag Leaves

<div><p>D1 protein in the PSII reaction center is the major target of photodamage, and it exhibits the highest turnover rate among all the thylakoid proteins. In this paper, rice <i>psf</i> (premature senescence of flag leaves) mutant and its wild type were used to investigate the genotype-dependent alteration in PSII photo-damage and D1 protein turnover during leaf senescence and its relation to ABA accumulation in senescent leaves. The symptom and extent of leaf senescence of the <i>psf</i> mutant appeared to be sunlight-dependent under natural field condition. The <i>psf</i> also displayed significantly higher levels of ABA accumulation in senescent leaves than the wild type. However, the premature senescence lesion of <i>psf</i> leaves could be alleviated by shaded treatment, concomitantly with the strikingly suppressed ABA level in the shaded areas of flag leaves. The change in ABA concentration contributed to the regulation of shade-delayed leaf senescence. The participation of ABA in the timing of senescence initiation and in the subsequent rate of leaf senescence was closely associated with PSII photodamage and D1 protein turnover during leaf senescence, in which the transcriptional expression of several key genes (<i>psbA</i>, <i>psbB</i>, <i>psbC</i> and <i>OsFtsH2</i>) involved in D1 protein biosynthesis and PSII repair cycle was seriously suppressed by the significantly increased ABA level. This response resulted in the low rate of D1 protein synthesis and impaired repair recovery in the presence of ABA. The <i>psf</i> showed evidently decreased D1 protein amount in the senescent leaves. Both the inhibition of de novo synthesized D1 protein and the slow rate of proteolytic removal for the photodamaged D1 protein was among the most crucial steps for the linkage between light-dependent leaf senescence and the varying ABA concentration in <i>psf</i> mutant leaves. <i>OsFtsH2</i> transcriptional expression possibly played an important role in the regulation of D1 protein turnover and PSII repair cycle in relation to ABA mediated leaf senescence.</p></div