arXiv subjects

Mark Marron

Publications and source records attributed to Mark Marron.

17 recordsLinked to original sources

arXiv2026

A-JIT: Agentic Just-In-Time Software Construction

Traditional software delivery assumes a static paradigm: code is constructed prior to execution and deployed as a fixed artifact. We present Agentic Just-In-Time Software Construction (A-JIT), a paradigm that replaces static binaries with dynamic, software systems that can perpetually evolve to meet changing demands. In A-JIT, an application is an integrated assembly comprising code, a runtime harness, and an embedded AI agent that continuously observes system usage and live execution traces. Much like a traditional JIT compiler specializes machine code to runtime execution paths, A-JIT specializes software logic, workflows, and tool interfaces to meet the specific needs of the end-user. By integrating synthesis directly into the ambient application lifecycle, A-JIT enables applications to dynamically construct missing implementations, generate new capabilities on the fly, and continuously adapt to end-user behavior. We demonstrate how this model supports trace-driven human-AI co-construction and opens a new design space for adaptive, self-evolving software.

cs.SE↗

arXiv2026

Specifications for Humans, Agents, and Tooling

Specifications are the central mechanism for communicating intents, requirements, and constraints in software development. When they are explicit, clear, and reliable, they are an effective means for collaboration and cooperation. They allow for stakeholders to specify what they want, developers (or AI agents) to understand and implement the needed functionality, for clients to effectively use the system, and for automated tooling to validate the correctness for each of these steps. This tool paper outlines the Bosque API (BAPI) ecosystem, a software ecosystem designed to support modern spec-centered development. The BAPI specification language works in a fully polyglot ecosystem and provides a suite of features, including unparalleled expressivity, test generation, validation, and sand-boxing to support the complete application development lifecycle. These are critical to supporting emerging security and coding (both API implementation & usage) challenges presented by agentic AI systems.

cs.SE↗

arXiv2026

Embedded Made Easy -- Rethinking Embedded + Cloud Software Development (WIP)

The process of engineering and deploying applications in the edge/embedded space is massively complicated by the non-homogeneous nature of the software stack and the complexity of diagnostics & debugging. Often different languages and runtimes are used for different components of the system forcing designers to, irrevocably, make decisions about what components run on the periphery and what components run in the cloud. Further complications arise when handling and diagnosing failures in the system. Multiple stacks and, often, limited support for debugging complicate the already difficult task of analyzing distributed applications. This paper presents a work-in-progress vision for a unified language and runtime system that allows applications to scale seamlessly across the edge and cloud. Using a single language and runtime, applications can be developed and tested in a single environment, and then deployed to any component of the system -- from resource limited controllers to large cloud servers. Further, we outline how this retargetable stack can provide integrated diagnostics and debugging tools that allow developers to record and replay distributed events locally for analysis and debugging.

cs.DC↗

arXiv2026

An Empirical Analysis of the Glob Ecosystem

Glob patterns, a domain-specific language for structured string matching, are a foundational yet understudied component of modern software development embedded in everything from build scripts and configuration files to web server routes. However, this widespread use rests on a fragile foundation. Despite their ubiquity across tools and programming languages, a lack of standardization has led to a fragmented ecosystem rife with inconsistent behaviors, security vulnerabilities, and usability pitfalls. This paper presents the first empirical study of the glob ecosystem to quantify these challenges and chart a path toward robust solutions. Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs. Our findings reveal a stark divide between various globbing implementations and developer adoption. In addition, we document inconsistencies that hinder portability, reliability, and create security flaws. Our analysis reveals that security vulnerabilities are not corner cases, but a dominant concern, comprising almost a quarter of all developer discussions. We propose a path forward through standardization and introduce a formal specification for globs, GlobSpec, designed to resolve semantic ambiguities and bridge the expressiveness gap between current implementations and developer requirements.

cs.SE↗

arXiv2026

Toward an Agentic Infused Software Ecosystem

Fully leveraging the capabilities of AI agents in software development requires a rethinking of the software ecosystem itself. To this end, this paper outlines the creation of an Agentic Infused Software Ecosystem (AISE), that rests on three pillars. The first, of course, is the AI agents themselves, which in the past 5 years have moved from simple code completion and toward sophisticated independent development tasks, a trend which will only continue. The second pillar is the programming language and APIs (or tools) that these agents use to accomplish tasks, and increasingly, serve as the communication substrate that humans and AI agents interact and collaborate through. The final pillar is the runtime environment and ecosystem that agents operate within, and which provide the capabilities that programmatic agents use to interface with (and effect actions in) the external world. To realize the vision of AISE, all three pillars must be advanced in a holistic manner, and critically, in a manner that is synergistic for AI agents as they exist today, those that will exist in the future, and for the human developers that work alongside them.

cs.SE↗

arXiv2025

Independent Test Generation for RESTful APIs

Modern REST API testing relies on brittle sequences of calls to build system state. These multi-step tests suffer from non-determinism, poor scalability, and a "reachability tax" where a single failed setup step invalidates the entire test. We introduce TECTON, which breaks this cycle by replacing implicit state construction with explicit state synthesis of both the request payload and mock data it depends on. TECTON achieves this through two complementary mechanisms: it generates diverse, valid payloads directly, and it augments existing test mocks with realistic data so those payloads have valid system state to reference. Both mechanisms apply combinatorial testing to a new domain: the nested property space of Abstract Data Types (ADTs). TECTON decomposes complex API requests into primitive components to unleash LLMs on the more tractable subtasks of identifying equivalence classes of these primitives and generating representative values for them. It then uses LLMs to extract and inject state values via test mocks, enabling payloads to reference valid state. It recomposes these values into covering combinations to directly produce high-coverage test payloads. On standard RESTful benchmarks, TECTON achieves 70% average line coverage - a 20% absolute increase over sequence-based generators. It exposes 2x more runtime errors than any prior tool, including assertions and data constraint failures. TECTON's shift from sequencing API calls to synthesized payloads advances the state of the art in automated API validation.

cs.SE↗

arXiv2025

Catalpa: GC for a Low-Variance Software Stack

The performance of an application/runtime is usually conceptualized as a continuous function where, the lower the amount of memory/time used on a given workload, then the better the compiler/runtime is. However, in practice, good performance of an application is viewed as more of a binary function - either the application responds in under, say 100 ms, and provides a good user experience, or it takes a noticeable amount of time, leaving the user waiting and potentially abandoning the task. Thus, performance really means how often the application is fast enough to meet user expectations, leading industrial developers to focus on the 95th and 99th percentile tail-latencies as heavily, or moreso, than average response time. Our vision is to create a software stack that actively supports these needs via programming language and runtime system design. In this paper we present a novel garbage-collector design, the Catalpa collector, for the Bosque programming language and runtime. This allocator is designed to minimize latency and tail-latency variability while maintaining high-throughput and incurring small memory overheads. To achieve these goals we leverage various features of the Bosque language, including immutability and reference-cycle freedom, to construct a collector that has provably bounded collection pauses, incurs a fixed-constant memory overhead, and ensures starvation freedom for the application!

cs.PL↗

arXiv2024

An Effectively $\Omega(c)$ Language and Runtime

The performance of an application/runtime is usually thought of as a continuous function where, the lower the amount of memory/time used on a given workload, then the better the compiler/runtime is. However, in practice, good performance of an application is conceptually more of a binary function -- either the application responds in under, say 100ms, and is fast enough for a user to barely notice, or it takes a noticeable amount of time, leaving the user waiting and potentially abandoning the task. Thus, performance really means how often the application is fast enough to be usable, leading industrial developers to focus on the 95th and 99th percentile latencies as heavily, or moreso, than average response time. Unfortunately, tracking and optimizing for these high percentile latencies is difficult and often requires a deep understanding of the application, runtime, GC, and OS interactions. This is further complicated by the fact that tail performance is often only seen occasionally, and is specific to a certain workload or input, making these issues uniquely painful to handle. Our vision is to create a language and runtime that is designed to be $\Omega(c)$ in its performance -- that is, it is designed to have an effectively constant time to execute all operations, there is a constant fixed memory overhead for the application footprint, and the garbage-collector performs a constant amount of work per allocation + a (small) bounded pause for all collection/release operations.

cs.PL↗

arXiv2024

Toward Programming Languages for Reasoning: Humans, Symbolic Systems, and AI Agents

Integration, composition, mechanization, and AI assisted development are the driving themes in the future of software development. At their core these concepts are rooted in the increasingly important role of computing in our world, the desire to deliver functionality faster, with higher quality, and to empower more people to benefit from programmatic automation. These themes, and how they impact the human developers driving them, are the foundations for the next generation of programming languages. At first glance the needs of mechanization tools, AI agents, and human developers along with the various goals around development velocity, software quality, and software democratization are a broad and seemingly diverse set of needs. However, at their core is a single challenge that, once resolved, enables us to make radical progress in all of these areas. Our hypothesis is that, fundamentally, software development is a problem of reasoning about code and semantics. This is true for human developers implementing a feature, symbolic tools building models of application behavior, and even for language based AI agents as they perform tasks. While the particular aspects of reasoning that each agent struggles with varies to some degree, they share many common themes and, surprisingly, most mainstream languages extensively employ (anti)features that make this task harder or infeasible! This paper proposes a novel approach to this challenge -- instead of new language features or logical constructs, that add more complexity to what is already a problem of complexity, we propose radical simplification in the form of the Bosque platform and language.

cs.PL↗

arXiv2024

Looking into Black Box Code Language Models

Language Models (LMs) have shown their application for tasks pertinent to code and several code~LMs have been proposed recently. The majority of the studies in this direction only focus on the improvements in performance of the LMs on different benchmarks, whereas LMs are considered black boxes. Besides this, a handful of works attempt to understand the role of attention layers in the code~LMs. Nonetheless, feed-forward layers remain under-explored which consist of two-thirds of a typical transformer model's parameters. In this work, we attempt to gain insights into the inner workings of code language models by examining the feed-forward layers. To conduct our investigations, we use two state-of-the-art code~LMs, Codegen-Mono and Ploycoder, and three widely used programming languages, Java, Go, and Python. We focus on examining the organization of stored concepts, the editability of these concepts, and the roles of different layers and input context size variations for output generation. Our empirical findings demonstrate that lower layers capture syntactic patterns while higher layers encode abstract concepts and semantics. We show concepts of interest can be edited within feed-forward layers without compromising code~LM performance. Additionally, we observe initial layers serve as ``thinking'' layers, while later layers are crucial for predicting subsequent code tokens. Furthermore, we discover earlier layers can accurately predict smaller contexts, but larger contexts need critical later layers' contributions. We anticipate these findings will facilitate better understanding, debugging, and testing of code~LMs.

cs.AI↗

arXiv2024

A New Generation of Intelligent Development Environments

The practice of programming is undergoing a revolution with the introduction of AI assisted development (copilots) and the creation of new programming languages that are designed explicitly for tooling, analysis, and automation. Integrated Development Environments (IDEs) as they are currently conceptualized have not yet responded to these changes. They are still designed around the idea of a human programmer typing textual code into an editor window with the IDE providing assistance via the integration of various tools for syntax highlighting, compilation, debugging, and (maybe) code version control. This paper presents a vision for transforming the IDE from an Integrated Development Environment to an Intelligent Development Environment. The new IDE will be designed around the idea of a human programmer as the manager or curator of a software project who, rather than manually typing in code to implement a solution, will instead use the IDE to direct AI programming agents and/or automated tools to combine existing APIs, packages, and new code to implement the needed features. In this new model, the fundamental roles of the IDE are to 1) facilitate the communication between the human programmer and the AI agents and automated tools and 2) organize the workflow tasks needed to go from requirements gathering to the final tested and validated deployed feature. This paper presents a vision for the new Intelligent Development Environment based on a range of proof-of-concept high-value scenarios we have experimented with and discusses the challenges that remain to realizing these in a cohesive intelligent development experience.

cs.SE↗

arXiv2019

SafeStrings: Representing Strings as Structured Data

Strings are ubiquitous in code. Not all strings are created equal, some contain structure that makes them incompatible with other strings. CSS units are an obvious example. Worse, type checkers cannot see this structure: this is the latent structure problem. We introduce SafeStrings to solve this problem and expose latent structure in strings. Once visible, operations can leverage this structure to efficiently manipulate it; further, SafeStrings permit the establishment of closure properties. SafeStringsharness the subtyping and inheritance mechanics of their host language to create a natural hierarchy of string subtypes. SafeStrings define an elegant programming model over strings: the front end use of a SafeString is clear and uncluttered, with complexity confined inside the definition of a particular SafeString. They are lightweight, language-agnostic and deployable, as we demonstrate by implementing SafeStrings in TypeScript. SafeStrings reduce the surface area for cross-site scripting, argument selection defects, and they can facilitate fuzzing and analysis.

cs.PL↗

arXiv2019

Are My Invariants Valid? A Learning Approach

Ensuring that a program operates correctly is a difficult task in large, complex systems. Enshrining invariants -- desired properties of correct execution -- in code or comments can support maintainability and help sustain correctness. Tools that can automatically infer and recommend invariants can thus be very beneficial. However, current invariant-suggesting tools, such as Daikon, suffer from high rates of false positives, in part because they only leverage traced program values from available test cases, rather than directly exploiting knowledge of the source code per se. We propose a machine-learning approach to judging the validity of invariants, specifically of method pre- and post-conditions, based directly on a method's source code. We introduce a new, scalable approach to creating labeled invariants: using programs with large test-suites, we generate Daikon invariants using traces from subsets of these test-suites, and then label these as valid/invalid by cross-validating them with held-out tests. This process induces a large set of labels that provide a form of noisy supervision, which is then used to train a deep neural model, based on gated graph neural networks. Our model learns to map the lexical, syntactic, and semantic structure of a given method's body into a probability that a candidate pre- or post-condition on that method's body is correct and is able to accurately label invariants based on the noisy signal, even in cross-project settings. Most importantly, it performs well on a hand-curated dataset of invariants.

cs.SE↗

arXiv2018

McFly: Time-Travel Debugging for the Web

Time-traveling debuggers offer the promise of simplifying debugging by letting developers freely step forwards and backwards through a program's execution. However, web applications present multiple challenges that make time-travel debugging especially difficult. A time-traveling debugger for web applications must accurately reproduce all network interactions, asynchronous events, and visual states observed during the original execution, both while stepping forwards and backwards. This must all be done in the context of a complex and highly multithreaded browser runtime. At the same time, to be practical, a time-traveling debugger must maintain interactive speeds. This paper presents McFly, the first time-traveling debugger for web applications. McFly departs from previous approaches by operating on a high-level representation of the browser's internal state. This approach lets McFly provide accurate time-travel debugging - maintaining JavaScript and visual state in sync at all times - at interactive speeds. McFly's architecture is browser-agnostic, building on web standards supported by all major browsers. We have implemented McFly as an extension to the Microsoft Edge web browser, and core parts of McFly have been integrated into a time-traveling debugger product from Microsoft.

cs.PL↗

arXiv2015

Program Synthesis using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.

cs.PL↗

arXiv2012

Abstracting Runtime Heaps for Program Understanding

Modern programming environments provide extensive support for inspecting, analyzing, and testing programs based on the algorithmic structure of a program. Unfortunately, support for inspecting and understanding runtime data structures during execution is typically much more limited. This paper provides a general purpose technique for abstracting and summarizing entire runtime heaps. We describe the abstract heap model and the associated algorithms for transforming a concrete heap dump into the corresponding abstract model as well as algorithms for merging, comparing, and computing changes between abstract models. The abstract model is designed to emphasize high-level concepts about heap-based data structures, such as shape and size, as well as relationships between heap structures, such as sharing and connectivity. We demonstrate the utility and computational tractability of the abstract heap model by building a memory profiler. We then use this tool to check for, pinpoint, and correct sources of memory bloat from a suite of programs from DaCapo.

cs.PL↗

arXiv2012

Structural Analysis: Shape Information via Points-To Computation

This paper introduces a new hybrid memory analysis, Structural Analysis, which combines an expressive shape analysis style abstract domain with efficient and simple points-to style transfer functions. Using data from empirical studies on the runtime heap structures and the programmatic idioms used in modern object-oriented languages we construct a heap analysis with the following characteristics: (1) it can express a rich set of structural, shape, and sharing properties which are not provided by a classic points-to analysis and that are useful for optimization and error detection applications (2) it uses efficient, weakly-updating, set-based transfer functions which enable the analysis to be more robust and scalable than a shape analysis and (3) it can be used as the basis for a scalable interprocedural analysis that produces precise results in practice. The analysis has been implemented for .Net bytecode and using this implementation we evaluate both the runtime cost and the precision of the results on a number of well known benchmarks and real world programs. Our experimental evaluations show that the domain defined in this paper is capable of precisely expressing the majority of the connectivity, shape, and sharing properties that occur in practice and, despite the use of weak updates, the static analysis is able to precisely approximate the ideal results. The analysis is capable of analyzing large real-world programs (over 30K bytecodes) in less than 65 seconds and using less than 130MB of memory. In summary this work presents a new type of memory analysis that advances the state of the art with respect to expressive power, precision, and scalability and represents a new area of study on the relationships between and combination of concepts from shape and points-to analyses.

cs.PL↗