Research

Posts about QxWT written by Tom Schindl

Posts about A week at e4 written by Tom Schindl

Repairing broken tests in evolving software systems is an expensive and challenging task. One of the main challenges for test repair, in particular, is preserving the intent of the original tests in the repaired ones. To address this challenge, we propose a technique for test repair that models and considers the intent of a test when repairing it. Our technique first uses a search-based approach to generate repair candidates for the broken test. It then computes, for each candidate, its likelihood of preserving the original test intent. To do so, the technique characterizes such intent using the path conditions generated during a dynamic symbolic execution of the tests. Finally, the technique reports the best candidates to the developer as repair recommendations. We implemented and evaluated our technique on a benchmark of 91 broken tests in 4 open-source programs. Our results are promising, in that the technique was able to generate intentpreserving repair candidates for over 79% of those broken tests and rank the intent-preserving candidates as the first choice of repair recommendations for almost 70% of the broken tests.

AbstractWorking with models can lead to inconsistencies, e.g., due to erroneous or contradictory actions during concurrent modeling processes. Modern modeling environments typically tolerate inconsistencies and support their detection. However, at a later ...

Motion and path planning are fundamental to intelligent robotic systems, enabling navigation.

Mesh repair is a long-standing challenge in computer graphics and related fields. Converting defective meshes into watertight manifold meshes can greatly benefit downstream applications such as geometric processing, simulation, fabrication, learning, and synthesis. In this work, by assuming the model is visually correct, we first introduce three visual measures for visibility, orientation, and openness, based on ray-tracing. We then present a novel mesh repair framework incorporating visual measures with several critical steps, i.e., open surface closing, face reorientation, and global optimization, to effectively repair meshes with defects (e.g., gaps, holes, self-intersections, degenerate elements, and inconsistent orientations) and preserve visual appearances. Our method reduces unnecessary mesh complexity without compromising geometric accuracy or visual quality while preserving input attributes such as UV coordinates for rendering. We evaluate our approach on hundreds of models randomly selected from ShapeNet and Thingi10K, demonstrating its effectiveness and robustness compared to existing approaches.

Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently arise from retraining. To address these issues, we introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and Reintegration), a lifelong editing framework designed to support precise and low-cost model updates while preserving non-target knowledge. REPAIR mitigates the instability and conflicts of large-scale sequential edits through a closed-loop feedback mechanism coupled with dynamic memory management. Furthermore, by incorporating frequent knowledge fusion and enforcing strong locality guards, REPAIR effectively addresses the shortcomings of traditional distribution-agnostic approaches that often overlook unintended ripple effects. Our experiments demonstrate that REPAIR boosts editing accuracy by 10%-30% across multiple model families and significantly reduces knowledge forgetting. This work introduces a robust framework for developing reliable, scalable, and continually evolving LLMs.

In model-driven software engineering, models are used in all phases of the development process. These models must hold a high quality since the implementation of the systems they represent relies on them. Several existing tools reduce the burden of manually dealing with issues that affect models’ quality, such as syntax errors, model smells, and inadequate structures. However, these tools are often inflexible for customization and hard to extend. This paper presents a customizable and extensible model repair framework, PARMOREL, that enables users to deal with different issues in different types of models. The framework uses reinforcement learning to automatically find the best sequence of actions for repairing a broken model according to user preferences. As proof of concept, we repair syntactic errors in class diagrams taking into account a model distance metric and quality characteristics. In addition, we restore inter-model consistency between UML class and sequence diagrams while improving the coupling qualities of the sequence diagrams. Furthermore, we evaluate the approach on a large publicly available dataset and a set of real-world inspired models to show that PARMOREL can decide and pick the best solution to solve the issues present in the models to satisfy user preferences.

By: Andrew Miler - Discover how schools and teachers can better support students by creating a framework to ensure meaningful place-based learning.

in computational complexity theory, an abstract machine modeling computation as two parties (an untrusted but powerful ‘prover’; a trusted ‘verifier’ with bounded resources) exchanging messages to ascertain whether some string belongs to a languag

complexity class of problems with quasi-polynomial time algorithms

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M. EMERY and G. MOKOBODZKI, Sur le barycentre d’une probabilité dans une variété, Séminaire de Probabilités XXV, Lecture Notes in Mathematics, Vol. 1485, 1991, pp. 220-233.

A bizarre and complicated conspiracy theory has spread from the internet to President Trump's rallies.

What a study of 18 anti-government extremists arrested in connection with Jan. 6 tells us about QAnon and militia fusion.