Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE | Paper Citation Record

Observation 4a71926a-12b6-48d9-8c58-7d96827b10cf · outbound

This paper cites Minimal data, maximum clarity: A heuristic for explaining optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Minimal data, maximum clarity: A heuristic for explaining optimization,

Reference 1

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Observation ea6cd312-0391-481a-ab67-c418923b71ea · outbound

This paper cites Moot: a repository of many multi-objective optimiza- tion tasks,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Moot: a repository of many multi-objective optimiza- tion tasks,

Reference 2

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Observation 89a6f547-ffba-4904-9670-840d954c8580 · outbound

This paper cites Promisetune: Unveiling causally promising and explainable configuration tuning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Promisetune: Unveiling causally promising and explainable configuration tuning,

Reference 3

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Observation 83d70118-137f-43d6-a671-c757b75068b9 · outbound

This paper cites Can large language models improve se active learning via warm-starts?.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Can large language models improve se active learning via warm-starts?

Reference 4

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Observation d0d8b6da-fbac-4b84-9b1f-bf92d8cb09f9 · outbound

This paper cites Less Noise, More Signal: the DRR Effect for Better Optimizations of a Range of SE Tasks.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Less Noise, More Signal: the DRR Effect for Better Optimizations of a Range of SE Tasks

Reference 5

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Observation d42b2ac5-476d-4006-b228-e89bb0d305d6 · outbound

This paper cites Learning from very little data: On the value of landscape analysis for predicting software project health,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Learning from very little data: On the value of landscape analysis for predicting software project health,

Reference 6

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Observation 0101af79-dc0b-420d-8b09-2c9495b53f1a · outbound

This paper cites Finding faster configurations using flash,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Finding faster configurations using flash,

Reference 7

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Observation a21a9b8d-0e03-49e7-a8af-74f11d2d8831 · outbound

This paper cites Russell and P.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Russell and P

Reference 8

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Observation 20663359-07eb-44c4-a39b-8fa2759e19e6 · outbound

This paper cites A theoretical and empirical study of search-based testing: Local, global, and hybrid search,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A theoretical and empirical study of search-based testing: Local, global, and hybrid search,

Reference 9

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Observation 16f0c163-d1bb-456a-9f75-0f65a5d22855 · outbound

This paper cites Search-based software engineering: Trends, techniques and applications,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Search-based software engineering: Trends, techniques and applications,

Reference 10

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Observation b032bc2d-f246-4e72-8943-16e8b5e2deb0 · outbound

This paper cites Pyart: Python api recommendation in real-time,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Pyart: Python api recommendation in real-time,

Reference 11

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Observation 11f0ccf0-a153-40e6-b978-744bc9dd04b6 · outbound

This paper cites Optimization by simulated annealing,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Optimization by simulated annealing,

Reference 12

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Observation 68fba568-8fca-4da3-b606-3150448814cd · outbound

This paper cites The current state and future of search based software engineering,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE The current state and future of search based software engineering,

Reference 13

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Observation dac7adbf-f2fe-4d83-9f1c-e5d52324e081 · outbound

This paper cites Search-based fault local- ization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Search-based fault local- ization,

Reference 14

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Observation 42cf1962-d739-48d7-9a21-be77b5c12dab · outbound

This paper cites Evolutionsstrategie,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Evolutionsstrategie,

Reference 15

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Observation 7277586b-94a0-415f-8876-56ed6782b76d · outbound

This paper cites Test suite generation with the many independent objective (mio) algorithm,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Test suite generation with the many independent objective (mio) algorithm,

Reference 16

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Observation 05359d22-34c1-4d8d-b3b9-83d2b575e38a · outbound

This paper cites Iterated local search,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Iterated local search,

Reference 17

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Observation 2292415f-38cd-4eda-959c-0e115ca4017a · outbound

This paper cites A genetic programming based iterated local search for software project scheduling,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A genetic programming based iterated local search for software project scheduling,

Reference 18

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Observation e58f4676-5bd8-4dc8-bc8d-459225d7c21a · outbound

This paper cites Tabu search—part i,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Tabu search—part i,

Reference 19

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Observation f02ff132-71f2-4319-9a1c-7ff13340ad93 · outbound

This paper cites A tabu search algorithm for structural software testing,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A tabu search algorithm for structural software testing,

Reference 20

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Observation 8e83cc0f-bfc2-47b9-88be-66c92a3fcd67 · outbound

This paper cites Transfer learning for cross- company software defect prediction,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Transfer learning for cross- company software defect prediction,

Reference 21

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Observation 3237108e-42ad-4bbf-bc92-2b6107470781 · outbound

This paper cites How to “dodge.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE How to “dodge

Reference 22

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Observation 9119e904-7999-401b-a23f-621b64fedf36 · outbound

This paper cites an unresolved cited work.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Unresolved cited work

Reference 23

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Observation c2f70f60-2d0c-4efa-908a-9a7122756749 · outbound

This paper cites Genprog: A generic method for automatic software repair,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Genprog: A generic method for automatic software repair,

Reference 24

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Observation 135cd72f-9901-4126-8ae2-a62621389c56 · outbound

This paper cites An empirical study of meta-and hyper-heuristic search for multi-objective release planning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE An empirical study of meta-and hyper-heuristic search for multi-objective release planning,

Reference 25

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Observation 0e969017-53db-4125-8b73-0ed4d4ccc11e · outbound

This paper cites From recombination of genes to the estimation of distributions i. binary parameters,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE From recombination of genes to the estimation of distributions i. binary parameters,

Reference 26

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Observation 22700547-13e0-45a8-aa17-baca7c178f56 · outbound

This paper cites Test data generation for mutation testing based on markov chain usage model and estimation of distribution algorithm,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Test data generation for mutation testing based on markov chain usage model and estimation of distribution algorithm,

Reference 27

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Observation da20b2c2-9f0b-41f5-8855-e4b289c0339e · outbound

This paper cites Particle swarm optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Particle swarm optimization,

Reference 28

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Observation 1910308d-1529-4522-bded-ed3cc483b11e · outbound

This paper cites Learning seed-adaptive mutation strategies for greybox fuzzing,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Learning seed-adaptive mutation strategies for greybox fuzzing,

Reference 29

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Observation 779004dc-912e-4fbc-8c64-243b5d64bc16 · outbound

This paper cites Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,

Reference 30

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Observation 8202411b-3e61-4e8e-8f50-d2ad9c9e05ad · outbound

This paper cites Easy over hard: A case study on deep learning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Easy over hard: A case study on deep learning,

Reference 31

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Observation 83a9d22e-a525-4fcf-ab81-7cbeb33778d4 · outbound

This paper cites Sequential model-based optimization for general algorithm configuration,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Sequential model-based optimization for general algorithm configuration,

Reference 32

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Observation 8bf7accf-f476-41cf-8967-8d1e93c5c75b · outbound

This paper cites How low can you go? the data-light SE challenge,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE How low can you go? the data-light SE challenge,

Reference 33

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Observation b1a9081c-b741-4df2-ac99-d10d687dd2cd · outbound

This paper cites Algorithms for hyper-parameter optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Algorithms for hyper-parameter optimization,

Reference 34

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Observation 48fc0607-e01e-4e73-9b70-0f846b5ceda5 · outbound

This paper cites Efficient compiler autotuning via bayesian optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Efficient compiler autotuning via bayesian optimization,

Reference 35

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Observation 6304445a-9fc6-4a1d-b08e-253500ee1c77 · outbound

This paper cites “sampling.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE “sampling

Reference 36

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Observation 6606aeea-6d29-49f3-a4ab-3bafbc833ff2 · outbound

This paper cites Accuracy can lie: On the impact of surrogate model in configuration tuning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Accuracy can lie: On the impact of surrogate model in configuration tuning,

Reference 37

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Observation 5250f527-9552-4652-b75a-872d0c28f416 · outbound

This paper cites Random search for hyper-parameter opti- mization.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Random search for hyper-parameter opti- mization

Reference 38

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Observation aad304e7-1c0d-471f-85df-eabd12c7c3b9 · outbound

This paper cites A fast and elitist multiobjective genetic algorithm: Nsga-ii,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A fast and elitist multiobjective genetic algorithm: Nsga-ii,

Reference 39

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Observation bbeb0508-1b08-4af5-90b4-197f19cf7db8 · outbound

This paper cites Many-objective software remodularization using nsga-iii,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Many-objective software remodularization using nsga-iii,

Reference 40

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Observation 6e15cbcf-6eda-483d-ae93-626589aa8bdd · outbound

This paper cites A multi-objective test data generation approach for mutation testing of feature models,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A multi-objective test data generation approach for mutation testing of feature models,

Reference 41

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Observation 356aad16-2fbf-4124-88da-d27f369aefbb · outbound

This paper cites Spea2: Improving the strength pareto evolutionary algorithm,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Spea2: Improving the strength pareto evolutionary algorithm,

Reference 42

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Observation d9ecb7a1-1899-46e0-8e0c-eca72c13b31e · outbound

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Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Sms-emoa: Multiobjective selection based on dominated hypervolume,

Reference 43

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Observation a8106113-6448-4282-b650-1ba36ebf378e · outbound

This paper cites An empirical study on pareto based multi-objective feature selection for software defect prediction,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE An empirical study on pareto based multi-objective feature selection for software defect prediction,

Reference 44

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Observation c47384ef-b423-4edf-8cc0-2eb6389150ab · outbound

This paper cites Moea/d: A multiobjective evolutionary algorithm based on decomposition,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Moea/d: A multiobjective evolutionary algorithm based on decomposition,

Reference 45

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Observation 93e98a5c-44e3-40b9-9374-1fd5cc25a4a5 · outbound

This paper cites Compiler auto-tuning via critical flag selection,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Compiler auto-tuning via critical flag selection,

Reference 46

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Observation e7a9ec7b-dca3-49ec-a87a-2b90d082e0a4 · outbound

This paper cites Automatic database management system tuning through large-scale machine learn- ing,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Automatic database management system tuning through large-scale machine learn- ing,

Reference 47

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Observation 25ab67bf-b074-456e-ad9d-3a278d94b2bb · outbound

This paper cites On the value of user preferences in search-based software engineering: A case study in software product lines,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE On the value of user preferences in search-based software engineering: A case study in software product lines,

Reference 48

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Observation 10c0548c-effe-4681-a5c9-b8cbf3380991 · outbound

This paper cites An end-to-end automatic cloud database tuning system using deep reinforcement learning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE An end-to-end automatic cloud database tuning system using deep reinforcement learning,

Reference 49

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Observation b17d52f7-1a94-4857-a939-ef1e70f92780 · outbound

This paper cites Iterative generation of adversarial example for deep code models,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Iterative generation of adversarial example for deep code models,

Reference 50

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Observation 6628f77d-0387-47fb-a0cf-ed1b95057af9 · outbound

This paper cites No free lunch theorems for optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE No free lunch theorems for optimization,

Reference 51

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Observation 8c52c74b-0572-4792-bceb-46fb66cd2698 · outbound

This paper cites Toward automated algorithm design: A survey and practical guide to meta- black-box-optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Toward automated algorithm design: A survey and practical guide to meta- black-box-optimization,

Reference 52

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Observation d6f6e265-6ddc-45a6-80eb-f36cc2ac5706 · outbound

This paper cites How efficient is llm-generated code? a rigorous & high-standard benchmark,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE How efficient is llm-generated code? a rigorous & high-standard benchmark,

Reference 53

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Observation a10d6a91-0025-429f-9ddf-94d626dd855b · outbound

This paper cites Simpler hyperparameter optimization for software analytics: Why, how, when?.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Simpler hyperparameter optimization for software analytics: Why, how, when?

Reference 54

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Observation a5737340-ac4c-4283-ac7b-33b3c213bcc1 · outbound

This paper cites Analysing the fitness landscape of search-based software testing problems,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Analysing the fitness landscape of search-based software testing problems,

Reference 55

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Observation e9c70ace-ff79-4f65-bfc3-699e38920312 · outbound

This paper cites Causes and effects of fitness landscapes in unit test generation,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Causes and effects of fitness landscapes in unit test generation,

Reference 56

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Observation 9b5757d4-d8ee-4229-a788-ea5761ec1a92 · outbound

This paper cites Large language models for software engi- neering: A systematic literature review,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Large language models for software engi- neering: A systematic literature review,

Reference 57

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Observation 7953f753-fae6-4fb4-9673-e29884a5eb41 · outbound

This paper cites The design, analysis and interpretation of reper- tory grids,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE The design, analysis and interpretation of reper- tory grids,

Reference 58

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Observation 2dac029e-58bd-4baf-ade0-a75428e20baa · outbound

This paper cites Heuristics for systems engineering cost estimation,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Heuristics for systems engineering cost estimation,

Reference 59

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Observation d415e699-7725-440d-b693-4ee98307c351 · outbound

This paper cites Identifying self-admitted technical debts with jitterbug: A two-step approach,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Identifying self-admitted technical debts with jitterbug: A two-step approach,

Reference 60

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Observation 807ab23b-0345-4883-a577-45389c9edf9d · outbound

This paper cites Data quality matters: A case study on data label correctness for security bug report prediction,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Data quality matters: A case study on data label correctness for security bug report prediction,

Reference 61

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Observation b93456ae-3537-4974-b81f-f31d3bb4e1d4 · outbound

This paper cites Detecting false alarms from automatic static analysis tools: How far are we?.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Detecting false alarms from automatic static analysis tools: How far are we?

Reference 62

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Observation e4c9d465-7c7d-4563-a38c-b2e94ce6ab35 · outbound

This paper cites Data quality: Some comments on the nasa software defect datasets,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Data quality: Some comments on the nasa software defect datasets,

Reference 63

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Observation d5617754-ee84-4356-805e-881bb5732116 · outbound

This paper cites A large-scale empirical study of just-in-time quality assurance,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A large-scale empirical study of just-in-time quality assurance,

Reference 64

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Observation b6d07957-b681-42af-b604-41464d22ada6 · outbound

This paper cites Can llms re- place manual annotation of software engineering artifacts?.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Can llms re- place manual annotation of software engineering artifacts?

Reference 65

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Observation b07ba090-2eb7-47ad-a1c7-86abf457056f · outbound

This paper cites Hpobench: A collection of reproducible multi-fidelity benchmark problems for hpo,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Hpobench: A collection of reproducible multi-fidelity benchmark problems for hpo,

Reference 66

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Observation 79d4fb92-737b-42e4-a5c1-d813801459db · outbound

This paper cites Hebo: Pushing the limits of sample-efficient hyper-parameter optimisation,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Hebo: Pushing the limits of sample-efficient hyper-parameter optimisation,

Reference 67

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Observation 7b541729-7652-4ed6-8925-26c74cc3784e · outbound

This paper cites Scalable global optimization via local bayesian optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Scalable global optimization via local bayesian optimization,

Reference 68

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Observation 6880fdb8-49d7-42c9-83a4-9c9f975bb947 · outbound

This paper cites Unicorn: Reasoning about configurable system performance through the lens of causality,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Unicorn: Reasoning about configurable system performance through the lens of causality,

Reference 69

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Observation 5b2a7cb5-5b8a-4a6e-85c7-28bb381ba44e · outbound

This paper cites Llamatune: sample-efficient dbms configuration tuning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Llamatune: sample-efficient dbms configuration tuning,

Reference 70

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Observation 9a33d748-3543-47b1-a407-e4ba9b26c450 · outbound

This paper cites Available: https://doi.org/10.14778/3551793.3551844.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Available: https://doi.org/10.14778/3551793.3551844

Reference 71

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Observation 7b164406-4853-4356-bea1-9e5d6ef74709 · outbound

This paper cites Qtune: A query-aware database tuning system with deep reinforcement learning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Qtune: A query-aware database tuning system with deep reinforcement learning,

Reference 72

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Observation 9b11ee3a-c4ca-4946-b319-1e1e0bd97235 · outbound

This paper cites Mathematical discoveries from program search with large language models,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Mathematical discoveries from program search with large language models,

Reference 73

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Observation 21f9e3c2-e278-46d0-9dfb-2726eaae7d1c · outbound

This paper cites Reevo: Large language models as hyper-heuristics with reflective evolution,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Reevo: Large language models as hyper-heuristics with reflective evolution,

Reference 74

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Observation 4bb52b56-e18d-4e01-8594-4d9be69ff5ea · outbound

This paper cites Genetic programming as a means for programming computers by natural selection,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Genetic programming as a means for programming computers by natural selection,

Reference 75

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Observation a257bf7c-37e4-47c4-a94b-6748df19c40b · outbound

This paper cites An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints,

Reference 76

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Observation 1c2f3305-d383-4190-b1c5-6e85775c91ca · outbound

This paper cites The weights can be harmful: Pareto search versus weighted search in multi-objective search-based software engineering,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE The weights can be harmful: Pareto search versus weighted search in multi-objective search-based software engineering,

Reference 77

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Observation 78ad3a55-dd8d-4d8d-8ebd-9529fcf66e91 · outbound

This paper cites Mmo: meta multi-objectivization for software configuration tuning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Mmo: meta multi-objectivization for software configuration tuning,

Reference 78

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Observation 0852bacf-249b-4a9d-a78d-9f123355b1df · outbound

This paper cites Practical bayesian optimiza- tion of machine learning algorithms,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Practical bayesian optimiza- tion of machine learning algorithms,

Reference 79

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Observation e1962161-9984-4924-9da3-f1327066f9e0 · outbound

This paper cites Etune: Efficient con- figuration tuning for big-data software systems via configuration space reduction,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Etune: Efficient con- figuration tuning for big-data software systems via configuration space reduction,

Reference 80

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Observation bea21730-d860-443a-b449-f9e0ee07a777 · outbound

This paper cites Dually hierarchical drift adaptation for online configuration performance learning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Dually hierarchical drift adaptation for online configuration performance learning,

Reference 81

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Observation 0d0bf3b0-449a-47c9-a837-7aea181c764a · outbound

This paper cites Cotune: Co-evolutionary configuration tuning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Cotune: Co-evolutionary configuration tuning,

Reference 82

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Observation 11598357-a295-4e24-a026-85d06c24d897 · outbound

This paper cites Compiler autotuning through multiple- phase learning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Compiler autotuning through multiple- phase learning,

Reference 83

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Observation d830e7dd-8fa2-43ed-837e-3b4cfe4be96c · outbound

This paper cites Unveiling many faces of surrogate models for configuration tuning: A fitness landscape analysis perspec- tive,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Unveiling many faces of surrogate models for configuration tuning: A fitness landscape analysis perspec- tive,

Reference 84

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Observation 5b5ad87d-cd6d-4bf2-8649-130a6eebb0e7 · outbound

This paper cites Learning from delayed rewards,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Learning from delayed rewards,

Reference 85

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Observation fecda742-5ad0-4a0e-b0d8-47ab5db07782 · outbound

This paper cites Human-level control through deep reinforcement learning,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Human-level control through deep reinforcement learning,

Reference 86

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Observation fb8cd96e-66da-4654-8ad4-c2b6eb30697e · outbound

This paper cites Instance space analysis for algorithm testing: Methodology and software tools,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Instance space analysis for algorithm testing: Methodology and software tools,

Reference 87

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Observation 5a075462-63f2-4a54-8194-7c924e4ce10b · outbound

This paper cites Instance space analysis of search-based software testing,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Instance space analysis of search-based software testing,

Reference 88

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Observation e8d8ff3d-7a8d-49ae-a883-ec3443673d0f · outbound

This paper cites Bohb: Robust and efficient hyperparameter optimization at scale,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Bohb: Robust and efficient hyperparameter optimization at scale,

Reference 89

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Observation 41174038-b2d0-4d91-af42-2d0865b81bda · outbound

This paper cites DEHB: Evolutionary hyberband for scalable, robust and efficient hyperparameter optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE DEHB: Evolutionary hyberband for scalable, robust and efficient hyperparameter optimization,

Reference 90

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Observation cf2084e1-41fd-4344-910e-1dda105c6ec8 · outbound

This paper cites Using Large Language Models for Hyperparameter Optimization.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Using Large Language Models for Hyperparameter Optimization

Reference 91

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Observation b4b812c1-9203-4f39-93a1-3d7586a0cdee · outbound

This paper cites Language model crossover: Variation through few-shot prompting,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Language model crossover: Variation through few-shot prompting,

Reference 92

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Observation 956e5c97-a76f-4c11-b887-dad1c02d1cfb · outbound

This paper cites Yahpo gym-an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Yahpo gym-an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization,

Reference 93

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Observation e8707993-6ee3-4acc-9ba0-0188a20ac99a · outbound

This paper cites Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks,

Reference 94

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Observation bff7da0f-5e97-43d8-9608-213f3ba9c4e8 · outbound

This paper cites Efficient benchmarking of hyperparameter optimizers via surrogates,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Efficient benchmarking of hyperparameter optimizers via surrogates,

Reference 95

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Observation 950528ba-cc7a-4f6f-9924-f54e1ae1b0fd · outbound

This paper cites Efficient benchmarking of algorithm configurators via model- based surrogates,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Efficient benchmarking of algorithm configurators via model- based surrogates,

Reference 96

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Observation e89c4e94-a6ee-43c2-9218-75fef5e6fc33 · outbound

This paper cites A cluster analysis method for grouping means in the analysis of variance,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE A cluster analysis method for grouping means in the analysis of variance,

Reference 97

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Observation b896db23-e61d-4f0f-83c3-2c19892d3f73 · outbound

This paper cites Exploratory landscape analysis,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Exploratory landscape analysis,

Reference 98

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Observation 2ce1a512-6a1e-4eb3-8d0c-ad20ed459914 · outbound

This paper cites Detecting funnel structures by means of exploratory landscape analysis,.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Detecting funnel structures by means of exploratory landscape analysis,

Reference 99

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Observation ecdfd028-bc14-443f-9989-c22fc7a8996a · outbound

This paper cites Fitness distance correlation as a measure of problem difficulty for genetic algorithms.

Which Optimizer, At What Budget? A Tournament of Optimizers for Search-Based SE Fitness distance correlation as a measure of problem difficulty for genetic algorithms

Reference 100

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