Denys Poshyvanyk

William & Mary | WM · Department of Computer Science

One of the most common solutions adopted by software researchers to address code generation is by training Large Language Models (LLMs) on massive amounts of source code. Although a number of studies have shown that LLMs have been effectively evaluated on popular accuracy metrics (e.g., BLEU, CodeBleu), previous research has largely overlooked the...

Large Language Models (LLMs) for code are a family of high-parameter, transformer-based neural networks pre-trained on massive datasets of both natural and programming languages. These models are rapidly being employed in commercial AI-based developer tools, such as GitHub CoPilot. However, measuring and explaining their effectiveness on programmin...

While software engineers are optimistically adopting crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of crypto-detectors' effectiveness at finding crypto-API misuses in practice. This demo paper presents the technical details and usage scenarios of...

The demand for automated security analysis techniques, such as static analysis based security testing (SAST) tools continues to increase. To develop SASTs that are effectively leveraged by developers for finding vulnerabilities, researchers and tool designers must understand how developers perceive, select, and use SASTs, what they expect from the...

Neural Language Models of Code, or Neural Code Models (NCMs), are rapidly progressing from research prototypes to commercial developer tools. As such, understanding the capabilities and limitations of such models is becoming critical. However, the abilities of these models are typically measured using automated metrics that often only reveal a port...

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Gr...

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constra...

Many software bugs are reported manually, particularly bugs that manifest themselves visually in the user interface. End-users typically report these bugs via app reviewing websites, issue trackers, or in-app built-in bug reporting tools, if available. While these systems have various features that facilitate bug reporting (e.g., textual templates...

Many software bugs are reported manually, particularly bugs that manifest themselves visually in the user interface. End-users typically report these bugs via app reviewing websites, issue trackers, or in-app built-in bug reporting tools, if available. While these systems have various features that facilitate bug reporting (e.g., textual templates...

Deep learning (DL) techniques have been used to support several code-related tasks such as code summarization and bug-fixing. In particular, pre-trained transformer models are on the rise, also thanks to the excellent results they achieved in Natural Language Processing (NLP) tasks. The basic idea behind these models is to first pre-train them on a...

The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software devel...

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pac...

The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software devel...

One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually written bug reports can vary widely due to the effort required to include essential pieces of information, such as detailed reproduction steps (S2Rs). Despite the difficulty...

Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code subm...

One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually written bug reports can vary widely due to the effort required to include essential pieces of information, such as detailed reproduction steps (S2Rs). Despite the difficulty...

Deep learning (DL) techniques have been used to support several code-related tasks such as code summarization and bug-fixing. In particular, pre-trained transformer models are on the rise, also thanks to the excellent results they achieved in Natural Language Processing (NLP) tasks. The basic idea behind these models is to first pre-train them on a...

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constra...

Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the generated predictions, with substantial leaps forward made possible by deep learning (DL) models. However, code completion techniques are mostly evaluated in the scenari...

Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the generated predictions, with substantial leaps forward made possible by deep learning (DL) models. However, code completion techniques are mostly evaluated in the scenari...

The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software devel...

Code completion is one of the main features of modern Integrated Development Environments (IDEs). Its objective is to speed up code writing by predicting the next code token(s) the developer is likely to write. Research in this area has substantially bolstered the predictive performance of these techniques. However, the support to developers is sti...

This demo paper presents the technical details and usage scenarios of $\mu$SE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static ana...

Mobile application security has been a major area of focus for security research over the course of the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and perform...

Deep learning (DL) techniques are gaining more and more attention in the software engineering community. They have been used to support several code-related tasks, such as automatic bug fixing and code comments generation. Recent studies in the Natural Language Processing (NLP) field have shown that the Text-To-Text Transfer Transformer (T5) archit...

Mobile application security has been a major area of focus for security research over the course of the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and perform...

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, whe...

Code reviews are popular in both industrial and open source projects. The benefits of code reviews are widely recognized and include better code quality and lower likelihood of introducing bugs. However, since code review is a manual activity it comes at the cost of spending developers' time on reviewing their teammates' code. Our goal is to make t...

Home automation platforms enable consumers to conveniently automate various physical aspects of their homes. However, the security flaws in the platforms or integrated third-party products can have serious security and safety implications for the user’s physical environment. This article describes our systematic security evaluation of two popular s...

Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location with the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19) in San Diego, California. The goal of this workshop was...

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pac...

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constra...

Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually establishing trace links, researchers have developed automated approaches that draw relationships between pairs of textual software artifacts using similarity measures. Howev...

Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for progra...

Software testing is an essential part of the software lifecycle andrequires a substantial amount of time and effort. It has been esti-mated that software developers spend close to 50% of their time ontesting the code they write. For these reasons, a long standing goalwithin the research community is to (partially) automate softwaretesting. While se...

Mutation testing can be used to assess the fault-detection capabilities of a given test suite. To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject...

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big cod...

Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learni...

A major problem with user-written bug reports, indicated by developers and documented by researchers, is the (lack of high) quality of the reported steps to reproduce the bugs. Low-quality steps to reproduce lead to excessive manual effort spent on bug triage and resolution. This paper proposes Euler, an approach that automatically identifies and a...

Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forw...

Security researchers have recently discovered significant security and safety issues related to home automation and developed approaches to address them. Such approaches often face design and evaluation challenges which arise from their restricted perspective of home automation that is bounded by the IoT apps they analyze. The challenges of past wo...

A major problem with user-written bug reports, indicated by developers and documented by researchers, is the (lack of high) quality of the reported steps to reproduce the bugs. Low-quality steps to reproduce lead to excessive manual effort spent on bug triage and resolution. This paper proposes Euler, an approach that automatically identifies and a...

Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we...

Understanding software is an inherent requirement for many maintenance and evolution tasks. Without a thorough understanding of the code, developers would not be able to fix bugs or add new features timely. Measuring code understandability might be useful to guide developers in writing better code, and could also help in estimating the effort requi...

Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such as writing commit messages, generating comments and detecting vulnerabilities among others. One of the long lasting dreams of applying DL to source code is the possibility...

The process of developing a mobile application typically starts with the ideation and conceptualization of its user interface. This concept is then translated into a set of mock-ups to help determine how well the user interface embodies the intended features of the app. After the creation of mock-ups developers then translate it into an app that ru...

Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a clear need for better, possibly customized, mutation operators and strategies. While some recent papers have trie...

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code....

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learni...

Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we...

The number of mobile devices sold worldwide has exponentially increased in recent years, surpassing that of personal computers in 2011. Such devices daily download and run millions of apps that take advantage of modern hardware features (e.g., multi-core processors, large Organic Light-Emitting Diode—OLED—screens, etc.) to offer exciting user exper...

Mobile applications have become a popular software development domain in recent years due in part to a large user base, capable hardware, and accessible platforms. However, mobile developers also face unique challenges, including pressure for frequent releases to keep pace with rapid platform evolution, hardware iteration, and user feedback. Due to...

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learni...

Mobile applications have become a popular software development domain in recent years due in part to a large user base, capable hardware, and accessible platforms. However, mobile developers also face unique challenges, including pressure for frequent releases to keep pace with rapid platform evolution, hardware iteration, and user feedback. Due to...

Test Case Prioritization (TCP) is an important component of regression testing, allowing for earlier detection of faults or helping to reduce testing time and cost. While several TCP approaches exist in the research literature, a growing number of studies have evaluated them against synthetic software defects, called mutants. Hence, it is currently...

Mobile application security has been one of the major areas of security research in the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and performance, and are he...

Dead code is a bad smell and it appears to be widespread in open-source and commercial software systems. Surprisingly, dead code has received very little empirical attention from the software engineering research community. In this paper, we present a multi-study investigation with an overarching goal to study, from the perspective of researchers a...

Unreadable code could compromise program comprehension, and it could cause the introduction of bugs. Code consists of mostly natural language text, both in identifiers and comments, and it is a particular form of text. Nevertheless, the models proposed to estimate code readability take into account only structural aspects and visual nuances of sour...

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many...

Techniques to automatically identify the stereotypes of different software artifacts (e.g., classes, methods, commits) were previously presented. Those approaches utilized the techniques to support comprehension of software artifacts, but those stereotype-based approaches were not designed to consider the structure and purpose of unit tests, which...

Empirical studies have provided ample evidence that low code quality is generally associated with lower maintainability. For this reason, tools have been developed to automatically detect design flaws (e.g., code smells). However, these tools are not able to prevent the introduction of design flaws. This means that the code has to experience a qual...

Assessing the similarity between code components plays a pivotal role in a number of Software Engineering (SE) tasks, such as clone detection, impact analysis, refactoring, etc. Code