Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1804.02433

Traceability in the Wild: Automatically Augmenting Incomplete Trace Links

Michael Rath·Jacob Rendall·Jin L. C. Guo·Jane Cleland-Huang·Patrick Maeder
View original source ↗Read source document ↗Export metadata ↓

Abstract

Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%

Explore related subjects

cs.SE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Michael Rath, Jacob Rendall, Jin L. C. Guo, Jane Cleland-Huang, Patrick Maeder. 2018-04-06. Traceability in the Wild: Automatically Augmenting Incomplete Trace Links. https://arxiv.org/abs/1804.02433

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Search-based Trace Diagnostic for Cyber-Physical Systems

Cyber-physical systems (CPS) development requires verifying whether system behaviors violate their requirements. This analysis often considers system behaviors expressed by execution traces and requirements expressed by signal-based temporal properties. When an execution trace violates a requirement, engineers must solve the trace diagnostic problem---they need to understand the cause of the breach. Automated trace diagnostic techniques aim to support engineers in the trace diagnostic activity. This paper proposes search-based trace diagnostic (SBTD), a novel trace diagnostic technique for CPS requirements. Unlike existing techniques, SBTD relies on evolutionary search. SBTD starts from a set of candidate diagnoses, applies an evolutionary algorithm to generate new candidate diagnoses (via mutation, recombination, and selection), and uses a fitness function to determine the qualities of these solutions. Then, a diagnostic generator step is performed to explain the cause of the trace violation. We implemented Diagnosis, an SBTD tool for signal-based temporal logic requirements expressed using the Hybrid Logic of Signals (HLS). We evaluated Diagnosis by performing 34 experiments for 17 trace-requirement combinations for property violations. We assessed the effectiveness of SBTD in producing informative diagnoses and its efficiency. Diagnosis achieved expert-aligned diagnoses for 29/34 experiments and scaled to the full HLS benchmark, whereas state-of-the-art literature remained restricted to a subset due to performance and language limitations. SBTD treats trace-checking as a black box, which makes the checker replaceable. Substituting our HLS checker for an STL monitor, e.g., RTAMT, reproduces on two requirements the diagnoses at two to three orders of magnitude lower per-check cost.

cs.SE↗
arXiv2026

Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking

Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but insecure. Progress in secure code generation has been hindered by benchmarks that are small, non-executable, leak mitigation details, or rely on noisy analyzers and subjective judgments, making it difficult to measure whether security improves without sacrificing correctness. We address these gaps with CodeSecEval, an execution-based benchmark for secure code generation, comprising 255 Python tasks spanning 77 CWE categories. Each task provides paired insecure and secure implementations together with executable functional and vulnerability-targeted security tests, enabling precise and reproducible evaluation of secure code generation and insecure-code repair. Building on CodeSecEval, we propose SecAwareCoder, an agent-based framework that shifts code generation toward secure-by-construction synthesis. SecAwareCoder performs task-adaptive threat modeling to identify security-sensitive regions and derive task-grounded vulnerability hypotheses, uses these hypotheses to guide both constraint-aware code generation and security-aware test synthesis, and leverages execution feedback for targeted refinement. Experiments across multiple LLM backbones show that SecAwareCoder consistently improves Pass@1 and security robustness over prompting and analyzer-driven baselines, narrowing the security--correctness gap in LLM code generation.

cs.SE↗
arXiv2026

Enabling Communication via APIs for Mainframe Applications

Mainframe systems continue to support critical applications across industries such as banking, retail, and healthcare. Exposing their functionality through Application Programming Interfaces (APIs) enables reuse and development of new applications, but identifying and implementing APIs for legacy code remains challenging. It requires understanding complex programs, separating dependent components, introducing new artifacts, and preserving functionality and Service Level Agreements (SLAs) such as Turnaround Time (TAT). We propose a framework for APIfication of legacy mainframe applications. Candidate APIs are identified from artifacts such as transactions, screens, control-flow blocks, inter-microservice calls, business rules, and data accesses. Static analyses, including liveness and reaching definitions, are then used to traverse the code and automatically compute API signatures consisting of request and response fields. We evaluated the framework through a qualitative survey of nine mainframe developers with an average of 15 years of experience, using the public GENAPP application and two industrial mainframe applications. The results show that the framework identifies additional candidate APIs and reduces implementation effort for APIfication. The API-signature computation has been incorporated into IBM watsonx Code Assistant for Z Refactoring Assistant. We further validated the identified APIs by executing them on an IBM Z mainframe system, demonstrating the practical viability of the approach.

cs.SE↗