We describe an online approach to SMT solver selection using nearest neighbor classification and runtime estimation. We implement and evaluate our approach with MedleySolver, finding that it makes nearly optimal selections and evaluates a dataset of queries three times faster than any indivdual solver.

Nikhil Pimpalkhare

University of California, Berkeley

The use of data-hungry deep learning algorithms to augment the performance of cloud-deployed software services calls for the constant relaying of private user data over a network that is susceptible to attack from malicious agents and also limits the extent to which data can be collated in a central repository to train centralized machine learning models. To encourage anonymized and decentralized training of models in such scenarios, We propose CrossPriv, a user-privacy sensitive model that enlists the characteristics of cross-silo federated software deployed across the clients participating in the cross-silo FL learning setup. We simulate the efficacy of the model by demonstrating the training of a deep learning model that can detect Pneumonia using X-Rays using training data hosted at two completely different silos, without sharing their raw data. We specify the client and server-side features of the deployed service whilst clearly defining the pipeline of cross-silo federated learning architecture.

Harshita Diddee

Guru Gobind Singh Indraprastha University

Bhrigu Kansra

Guru Gobind Singh Indraprastha University

India

Mosts of the neural synthesis construct encoder-decoder models to learn a probability distribution over the space of programs. Two drawbacks in such neural program synthesis are that the synthesis scale is relatively small and the correctness of the synthesis result cannot be guaranteed.We address these problems by constructing a framework, which analyzes and solves problems from three dimensions: program space description, model architecture, and result processing. Experiments show that the scalability and precision of synthesis are improved in every dimension.

Yating Zhang

National University of Defense Technology

Second place SRC - Undergraduate

Natural language comments are like bridges between human logic and software semantics. Developers use comments to describe the function, implementation, and property of code snippets. This kind of connections contains rich information, like the potential types of a variable and the pre-condition of a method, among other things. In this paper, we categorize comments and use natural language processing techniques to extract information from them. Based on the semantics of programming languages, different rules are built for each comment category to systematically propagate comments among code entities. Then we use the propagated comments to check the code usage and comments consistency. Our demo system finds 37 bugs in real-world projects, 30 of which have been confirmed by the developers. Except for bugs in the code, we also find 304 pieces of defected comments. The 12 of them are misleading and 292 of them are not correct. Moreover, among the 41573 pieces of comments we propagate, 87 comments are for private native methods which had neither code nor comments. We also conduct a user study where we find that propagated comments are as good as human-written comments in three dimensions of consistency, naturalness, and meaningfulness.

Xiangzhe Xu

Nanjing University

Third place SRC - Graduate

This paper presents a static vulnerability detection and patching framework at both source code and binary level. It collects source code known vulnerability information and automatically identifies and extracts binary vulnerability information. Using the known vulnerabilities, it matches similar vulnerable functions and filters out the ones that have been patched in the target program. For the vulnerable functions, the framework tries to generate hot patches by learning from the source code.

Zhengzi Xu

Nanyang Technological University

With the influx of Web 3.0 the focus in Big Data Analytics has shifted towards modelling highly interconnected data and analysing relationships between them. Graph databases befit the requirements of Big Data Analytics yet organizations still depend on relational databases. A major roadblock in the industry wide adoption of graph databases is that a standard query language is still in its inception stage hence withholding interoperability between the two technologies. In this research we propose a tool FLUX for translating relational database queries to graph database queries.

Chandan Sharma

Auckland University of Technology

Winner SRC - Graduate

GUI complexity posts a great challenge to the GUI implementation. According to our pilot study of crowdtesting bug reports, display issues such as text overlap, blurred screen, missing image always occur during GUI rendering on difference devices due to the software or hardware compatibility. They negatively influence the app usability, resulting in poor user experience. To detect these issues, we propose a novel approach, OwlEye, based on deep learning for modelling visual information of the GUI screenshot.Therefore, OwlEye can detect GUIs with display issues and also locate the detailed region of the issue in the given GUI for guiding developers to fix the bug. We manually construct a large-scale labelled dataset with 4,470 GUI screenshots with UI display issues. We develop a heuristics-based data augmentation method and a GAN-based data augmentation method for boosting the performance of our OwlEye. At present, the evaluation demonstrates that our OwlEye can achieve 85% precision and 84% recall in detecting UI display issues, and 90% accuracy in localizing these issues.

Zhe Liu

Laboratory for Internet Software Technologies, Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences

China