Identifying and Replicating Code Patterns Driving Performance Regressions in Software Systems

• URL: http://arxiv.org/abs/2504.05851v1
• Date: Tue, 08 Apr 2025 09:28:46 GMT
• Title: Identifying and Replicating Code Patterns Driving Performance Regressions in Software Systems
• Authors: Denivan Campos, Luana Martins, Emanuela Guglielmi, Michele Tucci, Daniele Di Pompeo, Simone Scalabrino, Vittorio Cortellessa, Dario Di Nucci, Rocco Oliveto,
• Abstract summary: Performance mutation testing introduces intentional defects to measure and enhance fault-detection capabilities.<n>A key challenge is understanding if generated mutants accurately reflect real-world performance issues.<n>This study evaluates and extends mutation operators for performance testing.
• Score: 7.030339427131108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: Performance regressions negatively impact execution time and memory usage of software systems. Nevertheless, there is a lack of systematic methods to evaluate the effectiveness of performance test suites. Performance mutation testing, which introduces intentional defects (mutants) to measure and enhance fault-detection capabilities, is promising but underexplored. A key challenge is understanding if generated mutants accurately reflect real-world performance issues. Goal: This study evaluates and extends mutation operators for performance testing. Its objectives include (i) collecting existing performance mutation operators, (ii) introducing new operators from real-world code changes that impact performance, and (iii) evaluating these operators on real-world systems to see if they effectively degrade performance. Method: To this aim, we will (i) review the literature to identify performance mutation operators, (ii) conduct a mining study to extract patterns of code changes linked to performance regressions, (iii) propose new mutation operators based on these patterns, and (iv) apply and evaluate the operators to assess their effectiveness in exposing performance degradations. Expected Outcomes: We aim to provide an enriched set of mutation operators for performance testing, helping developers and researchers identify harmful coding practices and design better strategies to detect and prevent performance regressions.

Related papers

• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.<n>We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations [53.0667196725616]
  Deep Reinforcement Learning (DRL) is a paradigm of artificial intelligence where an agent uses a neural network to learn which actions to take in a given environment.<n>DRL has recently gained traction from being able to solve complex environments like driving simulators, 3D robotic control, and multiplayer-online-battle-arena video games.<n>Numerous implementations of the state-of-the-art algorithms responsible for training these agents, like the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms, currently exist.
  arXiv  Detail & Related papers  (2025-03-28T16:25:06Z)
• FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies [26.50070102629617]
  We introduce FastFT, an innovative framework that leverages a trio of advanced strategies.<n>We develop a method to evaluate the novelty of generated transformation sequences.<n>We also combine novelty and performance to create a prioritized memory buffer.
  arXiv  Detail & Related papers  (2025-03-26T10:17:41Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions.<n>Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning? [1.9116784879310031]
  In deep Reinforcement Learning (RL), value functions are approximated using deep neural networks and trained via mean squared error regression objectives. Recent research has proposed an alternative approach, utilizing the cross-entropy classification objective. Our work seeks to empirically investigate the impact of such a replacement in an offline RL setup.
  arXiv  Detail & Related papers  (2024-06-10T14:25:11Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Diversity Enhanced Active Learning with Strictly Proper Scoring Rules [4.81450893955064]
  We study acquisition functions for active learning (AL) for text classification. We convert the Expected Loss Reduction (ELR) method to estimate the increase in (strictly proper) scores like log probability or negative mean square error. We show that the use of mean square error and log probability with BEMPS yields robust acquisition functions.
  arXiv  Detail & Related papers  (2021-10-27T05:02:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.