Reviewerly/exHarmony huggingface dataset:
Evaluating Differential Privacy in Federated Continual Learning

In recent years, the privacy-protecting framework Differential Privacy (DP) has achieved remarkable success and has been widely studied. However, there is a lack of work on DP in the area of Federated Continual Learning (FCL), which is a combination of Federated Learning (FL) and Continual Learning (CL). This paper presents a formal definition of DP-FCL and evaluates several DP-FCL methods based on Gaussian DP (GDP) and Individual DP (IDP). The experimental results indicate that gradient modification based CL strategies are not practical in DP-FCL. To the best of our knowledge, this is the first work to experimentally study DP-FCL, which can provide a reference for future research in this area.
GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench

With the emergence of Machine Learning, there has been a surge in leveraging its capabilities for problem-solving across various domains. In the code clone realm, the identification of type-4 or semantic clones has emerged as a crucial yet challenging task. Researchers aim to utilize Machine Learning to tackle this challenge, often relying on the Big-CloneBench dataset. However, it’s worth noting that BigCloneBench, originally not designed for semantic clone detection, presents several limitations that hinder its suitability as a comprehensive training dataset for this specific purpose. Furthermore, CLCDSA dataset suffers from a lack of reusable examples aligning with real-world software systems, rendering it inadequate for cross-language clone detection approaches. In this work, we present a comprehensive semantic clone and cross-language clone benchmark, GPTCloneBench <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> by exploiting SemanticCloneBench and OpenAI’s GPT-3 model. In particular, using code fragments from SemanticCloneBench as sample inputs along with appropriate prompt engineering for GPT-3 model, we generate semantic and cross-language clones for these specific fragments and then conduct a combination of extensive manual analysis, tool-assisted filtering, functionality testing and automated validation in building the benchmark. From 79,928 clone pairs of GPT-3 output, we created a benchmark with 37,149 true semantic clone pairs, 19,288 false semantic pairs(Type-1/Type-2), and 20,770 cross-language clones across four languages (Java, C, C#, and Python). Our benchmark is 15-fold larger than SemanticCloneBench, has more functional code examples for software systems and programming language support than CLCDSA, and overcomes BigCloneBench’s qualities, quantification, and language variety limitations. GPTCloneBench can be found here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
H-vectors: Hybrid Transformer and ResNet Based TDNN for Speaker Verification

Recently, the ResNet-based and Transformer-based Time Delay Neural Networks (TDNN) have achieved tremendous success in the area of speaker verification. However, ResNet-based TDNN models cannot capture global interaction information, while Transformer-based TDNN models struggle to explore multi-scale hierarchical pyramid convolutional features. This paper proposes a novel hybrid Transformer- and ResNet-based TDNN to learn speaker embeddings, named H-vectors, which integrates Transformer and ResNet via a macaron structure, fully exploiting global interaction and local temporal correlations. Specifically, a combination of Multi-Head Self-Attention and SE-Res2block is sandwiched between a pair of feed-forward modules. Additionally, the Gated Relative Position Bias is used to provide position information for Multi-Head Self-Attention, so that the relative position information between frames can be adaptively calibrated according to speech content. Extensive experiments on the VoxCeleb1 benchmark demonstrate that the proposed model achieves an EER of 0.79 and a MinDCF of 0.103, respectively, surpassing most of the state-of-the-art ResNet or Transformer-based TDNN models.
Summarize Me: The Future of Issue Thread Interpretation

Understanding issue threads is an essential aspect of software maintenance and development, aiding developers in effectively addressing and managing software-related issues. These threads typically contain an issue description, comments discussing possible solutions, and often culminate in a pull request where the proposed changes are elaborated. Even though they are crucial, understanding issue threads can be a lot of work because they are often long and complex, particularly in big projects. This paper, therefore, aims to automate the process of issue thread summarization using advanced AI models, specifically the GPT-3.5-Turbo, reducing the time spent and improving the efficiency of the interpretation process. Our approach taps into the potential of the zero-shot learning methodology, enabling the model to produce context-specific summaries without reliance on prior examples. Additionally, we have developed an algorithm that determines the most effective length for these summaries, which enhances their clarity and relevance. The performance of the model is assessed using automated metrics, including ROUGE and BART scores, for extractive and abstractive summary evaluation respectively. Further, we may like to add that summaries of around 30% to 40% of the total size of the issue thread appears to be sufficient, though it varies slightly from case to case. The model’s successful generation of brief, clear, and pertinent summaries not only boosts team communication and project management but also lays the groundwork for its future integration into a comprehensive tool for simplified exploration and comprehension of complex software repositories.
Investigation on the Bi-direction Fusion of Data and Knowledge in Prognostic and Health Management

Data and knowledge fusion are hot topics of machine learning, and the important working direction of Prognostic and Health Management technology (PHM). In order to crack up the schismatically situation of data and knowledge in industrial community, we propose the bi-direction fusion of data and knowledge in PHM including knowledge mining and knowledge embedding. We present an overall framework and conduct exploratory studies through actual project cases. In terms of knowledge mining, a KMAHP (knowledge mining analytic hierarchy process) test system decision method for structural health monitoring is proposed. And for knowledge embedding, a physical-based health baseline construction method is proposed for critical telemetry health status monitoring of aircraft. The results of the two cases are in line with expectations, which show that the proposed fusion framework and the concrete implementation plan are both effective exploration research and engineering practice for PHM.
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood.In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations.
Quaternary Categorization Strategy for Reconstructing High-Reflectivity Surface in Structured Light Illumination

Structured light illumination is widely applied for surface defect detection due to its advantages in terms of speed, precision, and non-contact capabilities. However, the high reflectivity of metal surfaces often results in the loss of point clouds, thus reducing the measurement accuracy. In this paper, we propose a novel quaternary categorization strategy to address the high-reflectivity issue. Firstly, we classify the pixels into four types according to the phase map characteristics. Secondly, we apply tailored optimization and reconstruction strategies to each type of pixel. Finally, we fuse point clouds from multi-type pixels to accomplish precise measurements of high-reflectivity surfaces. Experimental results show that our strategy effectively reduces the high-reflectivity error when measuring metal surfaces and exhibits stronger robustness against noise compared to the conventional method.
Towards Code Improvements Suggestions from Client Exception Analysis

Modern software development heavily relies on reusing third-party libraries; this makes developers more productive, but may also lead to misuses or other kinds of design issues. In this paper, we focus on the exceptional behavior of library methods, and propose to detect client code that may trigger such exceptional behavior. As we demonstrate on several examples of open-source projects, exceptional behavior in clients often naturally suggests improvements to the documentation, tests, runtime checks, and annotations of the clients.In order to automatically detect client calls that may trigger exceptional behavior in library methods, we show how to repurpose existing techniques to extract a method’s exception precondition—the condition under which the method throws an exception. To demonstrate the feasibility of our approach, we applied it to 1,523 open-source Java projects, where it found 4,115 cases of calls to library methods that may result in an exception. We manually analyzed 100 of these cases, confirming that the approach is capable of uncovering several interesting opportunities for code improvements.
A Machine Learning Approach to Convert Pseudo-Code to Domain-Specific Programming Language

The California Department of Energy oversees a set of standards and rules that new constructions (i.e., buildings) must adhere to within the State of California (the California Energy Code). Energy Code Ace (ECA) is a website that helps users plan their future construction in California and see if the plans for their structure comply with the California Energy Code. However, there are thousands of details and sections in the California Energy Code that need to be translated into questions and fields on the website for the user to fill out. To do this, the Department of Energy provides a web development team at Binary Evolution with XML schematics that contain details of each field and pseudo-code of the logic that fields should follow compared to the other fields on the ECA website. A very large portion of the developers’ job involves reading the pseudo-code and writing real code in a language developed by Binary Evolution. Reading and handwriting the code as described in the schematics can take up to hours of a developer’s time.This paper presents ECAi, a new approach for automatically generating executable code from pseudo-code schematics. ECAi uses a Decision Tree Classifier trained on 23,864 lines of pseudo-code and had to classify a line of pseudo-code into one of 13 categories achieving an 89.87% F1-score. Leveraging the classifier category, ECAi then calls a code-writing algorithm to parse the line of pseudo-code and writes the executable code that corresponds with the given pseudo-code line. The implementation of a prototype of the system shows a statistically significant (p=0.0116) decrease in the time it takes a developer to obtain an executable piece of code. ECAi has saved countless hours of development work during the maintenance workflow of ECA.
Repercussion of Image Compression on Satellite Image Classification using Deep Learning Models

Satellite image classification using Deep Learning (DL) algorithms is crucial for various applications such as Environmental Monitoring, Remote Sensing, and Urban Planning. The high-resolution nature of satellite images leads to large data volumes, resulting in computing challenges when classifying and transmitting the data over the internet. This paper addresses this challenge by reducing pixel size using Bicubic and Bilinear Interpolation. These techniques are known as image compression methods, which allow us to retain a significant portion of the original image information while reducing its size. Furthermore, adjusting the pixel size enables us to implement various levels of image compression supported by different DL classifiers, catering to diverse applications in NextG wireless networks or O-RAN. This pixel size reduction optimizes data transfer and speeds up satellite image transmission for Crop Monitoring and Land Cover Classification applications. After reducing the pixel, we employ different DL models such as CNN, ResNet, and stacked ensemble model, built by stacking three other EfficientNetB0 models to classify the low pixel images and compare the results using accuracy, precision, recall, and F1-Score. Staked EfficientNetB0 is the best performer among these, with an accuracy of 89.34% using a pixel size of 512x512. Our results show that we can reach up to 88.25% accuracy while maintaining a pixel size of 64x64, as our target is to reduce the image size while maintaining a tolerable accuracy so that network load and cost can be minimized.
Parsing Fortran-77 with proprietary extensions

Far from the latest innovations in software development, many organizations still rely on old code written in "obsolete" programming languages. Because this source code is old and proven it often contributes significantly to the continuing success of these organizations. Yet to keep the applications relevant and running in an evolving environment, they sometimes need to be updated or migrated to new languages or new platforms. One difficulty of working with these "veteran languages" is being able to parse the source code to build a representation of it. Parsing can also allow modern software development tools and IDEs to offer better support to these veteran languages. We initiated a project between our group and the Framatome company to help migrate old Fortran-77 with proprietary extensions (called Esope) into more modern Fortran. In this paper, we explain how we parsed the Esope language with a combination of island grammar and regular parser to build an abstract syntax tree of the code.
Large Language Model Empowered by Domain-Specific Knowledge Base for Industrial Equipment Operation and Maintenance

Industrial equipment operations and maintenance (IEOM) refers to ensuring the normal, safe, and reliable operation of industrial facilities, which covers condition monitoring, equipment maintenance, troubleshooting, repair and maintenance, and system optimization. Currently, the advancements in artificial intelligence have greatly improved the efficiency and effectiveness of IEOM. However, its robustness and generalization in practical applications still need to be improved. Large language models (LLMs) like ChatGPT have recently made breakthrough progress, demonstrating highly intelligent language comprehension capabilities. Therefore, they are expected to drive a new round of transformation in IEOM, promoting the automation and intelligence of the entire IEOM process. However, when using LLMs for practical industrial applications, existing LLMs have fatal limitations as they severely lack domain-specific expertise. This makes it difficult for LLMs to handle technical issues in the industrial field. To this end, this study explores a new solution: LLMs empowered by domain-specific knowledge base (LLM-DSKB). This paper provides a detailed introduction to the core components and implementation details of LLM-DSKB, including the knowledge base, text embedding, vectorized retrieval, etc. The performance of LLM-DSKB is analyzed using real industrial cases, and the results demonstrate that LLM-DSKB can provide more accurate, specific, and industrially relevant results compared to traditional LLMs. This solution will drive the application of LLMs in the industrial field, significantly enhancing the efficiency, effectiveness, and quality of IEOM.
Recommending Code Reviews Leveraging Code Changes with Structured Information Retrieval

Review comments are one of the main building blocks of modern code reviews. Manually writing code review comments could be time-consuming and technically challenging. Recently, an information retrieval (IR) based approach has been proposed to automatically recommend relevant code review comments for method-level code changes. However, this technique overlooks the structured items (e.g., class name, library information) from the source code and is applicable only for method-level changes. In this paper, we propose a novel technique for relevant review comments recommendation – RevCom – that leverages various code-level changes using structured information retrieval. RevCom uses different structured items from source code and can recommend relevant reviews for all types of changes (e.g., method-level and non-method-level). Our evaluation using three performance metrics show that RevCom outperforms both IR-based and DL-based baselines by up to 49.45% and 23.57% margins in BLEU score in recommending review comments. We find that RevCom can recommend review comments with an average BLEU score of ≈ 26.63%. According to Google’s AutoML Translation documentation, such a BLEU score indicates that the review comments can capture the original intent of the reviewers. All these findings suggest that RevCom can recommend relevant code reviews and has the potential to reduce the cognitive effort of human code reviewers.
Preparing Software Re-Engineering via Freehand Sketches in Virtual Reality

Re-architecting a software system requires significant preparation, e.g., to scope and design new modules with their boundaries and constituent classes. When planning an intended future state of a system as a re-engineering goal, engineers often fall recur to mechanisms such as freehand sketching (using a whiteboard). While this ensures flexibility and expressiveness, the sketches remain disconnected from the source code. The alternative, tool-supported diagramming on the other hand considerably restricts flexibility and impedes free-form communication.We present a method for preparing the architectural software re-engineering via freehand sketches in virtual reality (VR) that can be seamlessly integrated with the model structure of a software visualization and, thus, also the code of a system, for productive use: Engineers explore a subject system in the immersive visualization, while freehand sketching their insights and plans. Our concept automatically interprets sketched shapes and connects them to the system’s source code, and superimposes code-level references into a sketch to support engineers in reflecting on their sketches.We evaluated our method in an iterative interview-based case study with software developers from four different companies, where they planned a hypothetical re-engineering of an open-source software system.
Gravitational Wave Communications: A Survey

The earliest direct observation of gravitational waves was by LIGO in 2016, which is critical and leads to the confirmation of the preliminary speculations and theories related to gravitational waves. Ever since this detection and even before, physicists and engineers have advocated numerous methods to sense and generate gravitational waves. Moreover, such a discovery proposes the formation of an innovative means of information propagation owing to some similar characteristics of gravitational and electromagnetic waves. Therefore, gravitational wave communications offer a potential solution to the rise of congestion in the electromagnetic spectrum by expanding the capacity of communication systems. This survey paper explores the history of gravitational wave generation and reception from existing research such as papers, patents, and other publications following the postulation of general relativity. An overview of the documentation is presented, and various applications of gravitational wave communications are appraised. The practicality of such a futuristic communication system is analyzed by comparing the key features of both gravitational and electromagnetic waves and discussing their pros and cons.
A Case Study of DevOps Adoption within a Large Financial Organisation

DevOps is a collection of practices that reduces barriers between development and operations within software organisations, with the aim of increasing product release frequency and reliability. DevOps is increasingly important in the software industry. However, although existing research suggests that organisations face barriers to adoption, there are few case studies of how DevOps is adopted in practice. Therefore many organisations may face challenges in the transition to DevOps and lack guidance from prior experience as to successful strategies for managing the change.This paper presents a case study of DevOps adoption in a software development team within a large financial organisation. The study used a mixed-methods approach of surveys, interviews, and retrospectives to investigate the team’s experience. We collected evidence from 47 team members and document how the team has adopted the different practices. The findings highlight the factors influencing adoption, the benefits realised and the challenges faced. The results provide insights into practitioners’ perspective on DevOps adoption, as well as contributing to the evidence base on this practice in the literature as a basis for future research.