GeNN: a code generation framework for accelerated brain simulations

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}

Rqc: a bioconductor package for quality control of high-throughput sequencing data

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOAs sequencing costs drop with the constant improvements in the field, next-generation sequencing becomes one of the most used technologies in biological research. Sequencing technology allows the detailed characterization of events at the molecular level,87CN2114FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO2013/24801-2sem informaçã

On-the-Fly Syntax Highlighting using Neural Networks

With the presence of online collaborative tools for software developers, source code is shared and consulted frequently, from code viewers to merge requests and code snippets. Typically, code highlighting quality in such scenarios is sacrificed in favor of system responsiveness. In these on-the-fly settings, performing a formal grammatical analysis of the source code is not only expensive, but also intractable for the many times the input is an invalid derivation of the language. Indeed, current popular highlighters heavily rely on a system of regular expressions, typically far from the specification of the language's lexer. Due to their complexity, regular expressions need to be periodically updated as more feedback is collected from the users and their design unwelcome the detection of more complex language formations. This paper delivers a deep learning-based approach suitable for on-the-fly grammatical code highlighting of correct and incorrect language derivations, such as code viewers and snippets. It focuses on alleviating the burden on the developers, who can reuse the language's parsing strategy to produce the desired highlighting specification. Moreover, this approach is compared to nowadays online syntax highlighting tools and formal methods in terms of accuracy and execution time, across different levels of grammatical coverage, for three mainstream programming languages. The results obtained show how the proposed approach can consistently achieve near-perfect accuracy in its predictions, thereby outperforming regular expression-based strategies.Comment: Accepted for publication in the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022

Understanding Large Language Model Based Fuzz Driver Generation

Fuzz drivers are a necessary component of API fuzzing. However, automatically generating correct and robust fuzz drivers is a difficult task. Compared to existing approaches, LLM-based (Large Language Model) generation is a promising direction due to its ability to operate with low requirements on consumer programs, leverage multiple dimensions of API usage information, and generate human-friendly output code. Nonetheless, the challenges and effectiveness of LLM-based fuzz driver generation remain unclear. To address this, we conducted a study on the effects, challenges, and techniques of LLM-based fuzz driver generation. Our study involved building a quiz with 86 fuzz driver generation questions from 30 popular C projects, constructing precise effectiveness validation criteria for each question, and developing a framework for semi-automated evaluation. We designed five query strategies, evaluated 36,506 generated fuzz drivers. Furthermore, the drivers were compared with manually written ones to obtain practical insights. Our evaluation revealed that: while the overall performance was promising (passing 91% of questions), there were still practical challenges in filtering out the ineffective fuzz drivers for large scale application; basic strategies achieved a decent correctness rate (53%), but struggled with complex API-specific usage questions. In such cases, example code snippets and iterative queries proved helpful; while LLM-generated drivers showed competent fuzzing outcomes compared to manually written ones, there was still significant room for improvement, such as incorporating semantic oracles for logical bugs detection.Comment: 17 pages, 14 figure

Topic driven testing

Modern interactive applications offer so many interaction opportunities that automated exploration and testing becomes practically impossible without some domain specific guidance towards relevant functionality. In this dissertation, we present a novel fundamental graphical user interface testing method called topic-driven testing. We mine the semantic meaning of interactive elements, guide testing, and identify core functionality of applications. The semantic interpretation is close to human understanding and allows us to learn specifications and transfer knowledge across multiple applications independent of the underlying device, platform, programming language, or technology stack—to the best of our knowledge a unique feature of our technique. Our tool ATTABOY is able to take an existing Web application test suite say from Amazon, execute it on ebay, and thus guide testing to relevant core functionality. Tested on different application domains such as eCommerce, news pages, mail clients, it can trans- fer on average sixty percent of the tested application behavior to new apps—without any human intervention. On top of that, topic-driven testing can go with even more vague instructions of how-to descriptions or use-case descriptions. Given an instruction, say “add item to shopping cart”, it tests the specified behavior in an application–both in a browser as well as in mobile apps. It thus improves state-of-the-art UI testing frame- works, creates change resilient UI tests, and lays the foundation for learning, transfer- ring, and enforcing common application behavior. The prototype is up to five times faster than existing random testing frameworks and tests functions that are hard to cover by non-trained approaches.Moderne interaktive Anwendungen bieten so viele Interaktionsmöglichkeiten, dass eine vollständige automatische Exploration und das Testen aller Szenarien praktisch unmöglich ist. Stattdessen muss die Testprozedur auf relevante Kernfunktionalität ausgerichtet werden. Diese Arbeit stellt ein neues fundamentales Testprinzip genannt thematisches Testen vor, das beliebige Anwendungen u ̈ber die graphische Oberfläche testet. Wir untersuchen die semantische Bedeutung von interagierbaren Elementen um die Kernfunktionenen von Anwendungen zu identifizieren und entsprechende Tests zu erzeugen. Statt typischen starren Testinstruktionen orientiert sich diese Art von Tests an menschlichen Anwendungsfällen in natürlicher Sprache. Dies erlaubt es, Software Spezifikationen zu erlernen und Wissen von einer Anwendung auf andere zu übertragen unabhängig von der Anwendungsart, der Programmiersprache, dem Testgerät oder der -Plattform. Nach unserem Kenntnisstand ist unser Ansatz der Erste dieser Art. Wir präsentieren ATTABOY, ein Programm, das eine existierende Testsammlung für eine Webanwendung (z.B. für Amazon) nimmt und in einer beliebigen anderen Anwendung (sagen wir ebay) ausführt. Dadurch werden Tests für Kernfunktionen generiert. Bei der ersten Ausführung auf Anwendungen aus den Domänen Online Shopping, Nachrichtenseiten und eMail, erzeugt der Prototyp sechzig Prozent der Tests automatisch. Ohne zusätzlichen manuellen Aufwand. Darüber hinaus interpretiert themen- getriebenes Testen auch vage Anweisungen beispielsweise von How-to Anleitungen oder Anwendungsbeschreibungen. Eine Anweisung wie "Fügen Sie das Produkt in den Warenkorb hinzu" testet das entsprechende Verhalten in der Anwendung. Sowohl im Browser, als auch in einer mobilen Anwendung. Die erzeugten Tests sind robuster und effektiver als vergleichbar erzeugte Tests. Der Prototyp testet die Zielfunktionalität fünf mal schneller und testet dabei Funktionen die durch nicht spezialisierte Ansätze kaum zu erreichen sind

Configuring Test Generators using Bug Reports: A Case Study of GCC Compiler and Csmith

The correctness of compilers is instrumental in the safety and reliability of other software systems, as bugs in compilers can produce executables that do not reflect the intent of programmers. Such errors are difficult to identify and debug. Random test program generators are commonly used in testing compilers, and they have been effective in uncovering bugs. However, the problem of guiding these test generators to produce test programs that are more likely to find bugs remains challenging. In this paper, we use the code snippets in the bug reports to guide the test generation. The main idea of this work is to extract insights from the bug reports about the language features that are more prone to inadequate implementation and using the insights to guide the test generators. We use the GCC C compiler to evaluate the effectiveness of this approach. In particular, we first cluster the test programs in the GCC bugs reports based on their features. We then use the centroids of the clusters to compute configurations for Csmith, a popular test generator for C compilers. We evaluated this approach on eight versions of GCC and found that our approach provides higher coverage and triggers more miscompilation failures than the state-of-the-art test generation techniques for GCC.Comment: The 36th ACM/SIGAPP Symposium on Applied Computing, Software Verification and Testing Track (SAC-SVT'21

"It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools

While revolutionary AI-powered code generation tools have been rising rapidly, we know little about how and how to help software developers form appropriate trust in those AI tools. Through a two-phase formative study, we investigate how online communities shape developers' trust in AI tools and how we can leverage community features to facilitate appropriate user trust. Through interviewing 17 developers, we find that developers collectively make sense of AI tools using the experiences shared by community members and leverage community signals to evaluate AI suggestions. We then surface design opportunities and conduct 11 design probe sessions to explore the design space of using community features to support user trust in AI code generation systems. We synthesize our findings and extend an existing model of user trust in AI technologies with sociotechnical factors. We map out the design considerations for integrating user community into the AI code generation experience

"Always Nice and Confident, Sometimes wrong": Developer's Experiences Engaging Generative AI Chatbots Versus Human-Powered Q&A Platforms

Software engineers have historically relied on human-powered Q&A platforms, like Stack Overflow (SO), as coding aids. With the rise of generative AI, developers have adopted AI chatbots, such as ChatGPT, in their software development process. Recognizing the potential parallels between human-powered Q&A platforms and AI-powered question-based chatbots, we investigate and compare how developers integrate this assistance into their real-world coding experiences by conducting thematic analysis of Reddit posts. Through a comparative study of SO and ChatGPT, we identified each platform's strengths, use cases, and barriers. Our findings suggest that ChatGPT offers fast, clear, comprehensive responses and fosters a more respectful environment than SO. However, concerns about ChatGPT's reliability stem from its overly confident tone and the absence of validation mechanisms like SO's voting system. Based on these findings, we recommend leveraging each platform's unique features to improve developer experiences in the future