ConcernBERT: Learning Responsibilities Using Class Membership | Paper Citation Record

Observation 5e027fe7-9e43-47d0-b26d-41a4ca15c809 · outbound

This paper cites an unresolved cited work.

ConcernBERT: Learning Responsibilities Using Class Membership Unresolved cited work

Reference 1

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Observation d3159c61-06ef-49ba-a217-18027dd802c5 · outbound

This paper cites On the criteria to be used in de- composing systems into modules,.

ConcernBERT: Learning Responsibilities Using Class Membership On the criteria to be used in de- composing systems into modules,

Reference 2

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Observation 09c4e81e-81fd-4f1d-a36c-c6a159025c22 · outbound

This paper cites Automatic software clustering via latent semantic analysis,.

ConcernBERT: Learning Responsibilities Using Class Membership Automatic software clustering via latent semantic analysis,

Reference 3

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Observation 9cc3bc60-d004-47c5-8811-ca6780a54b76 · outbound

This paper cites Using latent seman- tic analysis to identify similarities in source code to support program understanding,.

ConcernBERT: Learning Responsibilities Using Class Membership Using latent seman- tic analysis to identify similarities in source code to support program understanding,

Reference 4

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Observation 90e7fdcb-85ee-4dbf-b641-96e48926bec5 · outbound

This paper cites Supporting program comprehension us- ing semantic and structural information,.

ConcernBERT: Learning Responsibilities Using Class Membership Supporting program comprehension us- ing semantic and structural information,

Reference 5

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Observation 8abb2884-cd68-4af9-b6a6-ad37e9996d06 · outbound

This paper cites An information retrieval approach to concept location in source code,.

ConcernBERT: Learning Responsibilities Using Class Membership An information retrieval approach to concept location in source code,

Reference 6

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Observation 51b04882-76d3-49ad-b100-9e605f70b577 · outbound

This paper cites Enriching reverse engineering with semantic clustering,.

ConcernBERT: Learning Responsibilities Using Class Membership Enriching reverse engineering with semantic clustering,

Reference 7

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Observation 2f7d6485-7150-49e7-8739-792c235c6cdc · outbound

This paper cites Semantic clustering: Identifying topics in source code,.

ConcernBERT: Learning Responsibilities Using Class Membership Semantic clustering: Identifying topics in source code,

Reference 8

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Observation 3c929352-3df3-4398-8cf7-93ccc91a26fd · outbound

This paper cites A theory of aspects as latent topics,.

ConcernBERT: Learning Responsibilities Using Class Membership A theory of aspects as latent topics,

Reference 9

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Observation 6fda5e9b-8f12-4d9c-8929-0ab4d67299ae · outbound

This paper cites Enhancing architec- tural recovery using concerns,.

ConcernBERT: Learning Responsibilities Using Class Membership Enhancing architec- tural recovery using concerns,

Reference 10

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Observation 7ef0b103-afcf-4c45-b7eb-5dc97eb787f3 · outbound

This paper cites Automatic software architecture re- covery: A machine learning approach,.

ConcernBERT: Learning Responsibilities Using Class Membership Automatic software architecture re- covery: A machine learning approach,

Reference 11

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Observation cb9230a0-c604-4da1-858b-1795537cbaad · outbound

This paper cites Recovering architectural design decisions,.

ConcernBERT: Learning Responsibilities Using Class Membership Recovering architectural design decisions,

Reference 12

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Observation eb7c1665-2df1-4b07-8658-0e74b71f81c6 · outbound

This paper cites Software architecture recovery with information fusion,.

ConcernBERT: Learning Responsibilities Using Class Membership Software architecture recovery with information fusion,

Reference 13

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Observation 95947776-457e-416a-a858-e1a21f472006 · outbound

This paper cites Using structural and semantic metrics to improve class cohesion,.

ConcernBERT: Learning Responsibilities Using Class Membership Using structural and semantic metrics to improve class cohesion,

Reference 14

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Observation f29d7883-e11f-4c4a-95a9-5846417035cf · outbound

This paper cites A two-step technique for ex- tract class refactoring,.

ConcernBERT: Learning Responsibilities Using Class Membership A two-step technique for ex- tract class refactoring,

Reference 15

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Observation 22b06d48-fdef-4528-806d-d371a146ba8c · outbound

This paper cites Identi- fying extract class refactoring opportunities us- ing structural and semantic cohesion measures,.

ConcernBERT: Learning Responsibilities Using Class Membership Identi- fying extract class refactoring opportunities us- ing structural and semantic cohesion measures,

Reference 16

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Observation 48700488-621a-4470-b55f-f9f72d330d91 · outbound

This paper cites Automating extract class refactor- ing: an improved method and its evaluation,.

ConcernBERT: Learning Responsibilities Using Class Membership Automating extract class refactor- ing: an improved method and its evaluation,

Reference 17

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Observation a33d939c-2ac9-4bac-b687-4b9efed14e3e · outbound

This paper cites Methodbook: Rec- ommending move method refactorings via re- lational topic models,.

ConcernBERT: Learning Responsibilities Using Class Membership Methodbook: Rec- ommending move method refactorings via re- lational topic models,

Reference 18

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Observation 341a054e-0ee7-478b-b9ba-dcb1fb2a8980 · outbound

This paper cites An approach of extracting god class exploiting both struc- tural and semantic similarity,.

ConcernBERT: Learning Responsibilities Using Class Membership An approach of extracting god class exploiting both struc- tural and semantic similarity,

Reference 19

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Observation 69324a4d-387f-486c-9345-51158a9ca8aa · outbound

This paper cites Using information retrieval based coupling measures for impact analysis,.

ConcernBERT: Learning Responsibilities Using Class Membership Using information retrieval based coupling measures for impact analysis,

Reference 20

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Observation 8d8f4011-ac59-4cef-8b2a-2b9c7421a531 · outbound

This paper cites New conceptual coupling and cohesion metrics for object-oriented systems,.

ConcernBERT: Learning Responsibilities Using Class Membership New conceptual coupling and cohesion metrics for object-oriented systems,

Reference 21

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Observation 78f0f2e0-3102-4bac-8deb-97deadae5057 · outbound

This paper cites Using rela- tional topic models to capture coupling among classes in object-oriented software systems,.

ConcernBERT: Learning Responsibilities Using Class Membership Using rela- tional topic models to capture coupling among classes in object-oriented software systems,

Reference 22

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Observation f92e1650-fe2f-4c69-96ea-0099e18bcc29 · outbound

This paper cites The concep- tual cohesion of classes,.

ConcernBERT: Learning Responsibilities Using Class Membership The concep- tual cohesion of classes,

Reference 23

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Observation b48590d4-a332-43f9-8093-958477150c21 · outbound

This paper cites Modeling class cohesion as mixtures of latent topics,.

ConcernBERT: Learning Responsibilities Using Class Membership Modeling class cohesion as mixtures of latent topics,

Reference 24

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Observation 7d41d420-0aec-4981-9061-16250cc6bcdd · outbound

This paper cites Topic-based soft- ware defect explanation,.

ConcernBERT: Learning Responsibilities Using Class Membership Topic-based soft- ware defect explanation,

Reference 25

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Observation b06e3857-32d1-4e33-896d-dfa91d7bc672 · outbound

This paper cites New conceptual cohesion met- rics: Assessment for software defect predic- tion,.

ConcernBERT: Learning Responsibilities Using Class Membership New conceptual cohesion met- rics: Assessment for software defect predic- tion,

Reference 26

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Observation c5ad498e-c882-4a06-bef8-5d4780285ddc · outbound

This paper cites Detection strategies: Metrics- based rules for detecting design flaws,.

ConcernBERT: Learning Responsibilities Using Class Membership Detection strategies: Metrics- based rules for detecting design flaws,

Reference 27

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Observation b8d8217b-4574-4698-a448-b5a565936294 · outbound

This paper cites A Semantic-Based Approach for Detecting and Decomposing God Classes.

ConcernBERT: Learning Responsibilities Using Class Membership A Semantic-Based Approach for Detecting and Decomposing God Classes

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-04T07:29:38.752293Z

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Observation 9aaa6cdf-0e28-44e5-9af1-e14eac6cd9d8 · outbound

This paper cites Concernbert: Learning respon- sibilities using class membership,.

ConcernBERT: Learning Responsibilities Using Class Membership Concernbert: Learning respon- sibilities using class membership,

Reference 29

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Observation abba53f0-6c54-427e-a1bd-82ed69730a59 · outbound

This paper cites Identification and appli- cation of extract class refactorings in object- oriented systems,.

ConcernBERT: Learning Responsibilities Using Class Membership Identification and appli- cation of extract class refactorings in object- oriented systems,

Reference 30

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Observation e449481d-bb63-4c8a-a4df-3fee4bbef14f · outbound

This paper cites Alzahrani,Using Clients to Support Extract Class Refactoring.

ConcernBERT: Learning Responsibilities Using Class Membership Alzahrani,Using Clients to Support Extract Class Refactoring

Reference 31

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Observation cd0d85d4-94ae-498f-a9db-c4039350b225 · outbound

This paper cites Extract class refactoring based on cohe- sion and coupling: A greedy approach,.

ConcernBERT: Learning Responsibilities Using Class Membership Extract class refactoring based on cohe- sion and coupling: A greedy approach,

Reference 32

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Observation 7b45297c-1d39-4143-9b61-383a39ef30e2 · outbound

This paper cites Deicide: Decomposing complex classes into re- sponsibility modules,.

ConcernBERT: Learning Responsibilities Using Class Membership Deicide: Decomposing complex classes into re- sponsibility modules,

Reference 33

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Observation c2ec558e-5536-4f28-96b5-6ca20a608d80 · outbound

This paper cites Codebert: A pre-trained model for programming and natural languages,.

ConcernBERT: Learning Responsibilities Using Class Membership Codebert: A pre-trained model for programming and natural languages,

Reference 34

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Observation e5c4bac2-c018-4a39-a201-e6e9cf47083d · outbound

This paper cites Graphcodebert: Pre-training code representations with data flow,.

ConcernBERT: Learning Responsibilities Using Class Membership Graphcodebert: Pre-training code representations with data flow,

Reference 35

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Observation 4de805b4-dc92-49f5-90e6-8a2ddd69b4ef · outbound

This paper cites Codet5+: Open code large language models for code understanding and generation,.

ConcernBERT: Learning Responsibilities Using Class Membership Codet5+: Open code large language models for code understanding and generation,

Reference 36

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Observation fe8bc884-f52a-491b-b071-3194498ab84c · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

ConcernBERT: Learning Responsibilities Using Class Membership Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 37

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Observation 7b8d6b12-6a74-440d-a3c4-40fceb553010 · outbound

This paper cites Indexing by latent semantic analysis,.

ConcernBERT: Learning Responsibilities Using Class Membership Indexing by latent semantic analysis,

Reference 38

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Observation 80d1dea8-f3d5-418a-9482-920ed8dc73ac · outbound

This paper cites La- tent dirichlet allocation,.

ConcernBERT: Learning Responsibilities Using Class Membership La- tent dirichlet allocation,

Reference 39

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Observation f47ab9c3-e65a-46fd-817e-0b9bb7c1e3f4 · outbound

This paper cites Distributed Representations of Sentences and Documents.

ConcernBERT: Learning Responsibilities Using Class Membership Distributed Representations of Sentences and Documents

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-04T07:29:38.754676Z

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Observation 49d3c005-fcf2-442e-929f-09a5cc510cb8 · outbound

This paper cites Learning and evaluating contextual embedding of source code,.

ConcernBERT: Learning Responsibilities Using Class Membership Learning and evaluating contextual embedding of source code,

Reference 41

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This paper cites Codeberta: A roberta-like pre-trained model for source code,.

ConcernBERT: Learning Responsibilities Using Class Membership Codeberta: A roberta-like pre-trained model for source code,

Reference 42

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Observation 506f19e3-bcb2-4936-b5ee-5726d97fe67e · outbound

This paper cites On the validity of pre-trained transformers for natural language processing in the software en- gineering domain,.

ConcernBERT: Learning Responsibilities Using Class Membership On the validity of pre-trained transformers for natural language processing in the software en- gineering domain,

Reference 43

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This paper cites Using bert for the detection of archi- tectural tactics in code,.

ConcernBERT: Learning Responsibilities Using Class Membership Using bert for the detection of archi- tectural tactics in code,

Reference 44

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This paper cites Lecture Notes in Com- puter Science.

ConcernBERT: Learning Responsibilities Using Class Membership Lecture Notes in Com- puter Science

Reference 45

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Observation 031bae81-ed78-4cb6-b952-403f00432e35 · outbound

This paper cites GraphCodeBERT: Pre-training Code Representations with Data Flow.

ConcernBERT: Learning Responsibilities Using Class Membership GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-04T07:29:38.761982Z

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Observation 5d23eed2-9f4d-45e4-b098-14865ca69c79 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Unixcoder: Unified cross-modal pre- training for code representation,

Reference 47

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Observation 2fd510c2-c3b3-43b4-8590-e66cea6876f5 · outbound

This paper cites SynCoBERT: Syntax-guided multi-modal con- trastive pre-training for code representation,.

ConcernBERT: Learning Responsibilities Using Class Membership SynCoBERT: Syntax-guided multi-modal con- trastive pre-training for code representation,

Reference 48

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Observation 901a201d-0b6c-4e2e-86f3-37ca8ed0f823 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership LineVul: A transformer-based line- level vulnerability prediction

Reference 49

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Observation 0a66fb6b-0171-40d8-be91-6da099a709b7 · outbound

This paper cites Codet5+: Open code large language models for code understanding and generation,.

ConcernBERT: Learning Responsibilities Using Class Membership Codet5+: Open code large language models for code understanding and generation,

Reference 50

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ConcernBERT: Learning Responsibilities Using Class Membership CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understand- ing and generation,

Reference 51

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This paper cites Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference,.

ConcernBERT: Learning Responsibilities Using Class Membership Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference,

Reference 52

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Observation 4d849e89-6bbd-4e2b-af55-f1af312d08b1 · outbound

This paper cites an unresolved cited work.

ConcernBERT: Learning Responsibilities Using Class Membership Unresolved cited work

Reference 53

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Observation 991519f5-9080-41a9-941d-9127abe59bdc · outbound

This paper cites Exploring topic coherence over many models and many topics,.

ConcernBERT: Learning Responsibilities Using Class Membership Exploring topic coherence over many models and many topics,

Reference 54

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Observation 1e467f3b-be15-4476-a296-c35833e8171b · outbound

This paper cites Deep learning code fragments for code clone detection,.

ConcernBERT: Learning Responsibilities Using Class Membership Deep learning code fragments for code clone detection,

Reference 55

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Observation de87cd52-9bb1-4da7-badd-6aae9468372e · outbound

This paper cites Convolutional neural networks over tree struc- tures for programming language processing,.

ConcernBERT: Learning Responsibilities Using Class Membership Convolutional neural networks over tree struc- tures for programming language processing,

Reference 56

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Observation 78591e07-d4ea-4104-9d6d-657bb5a3f7fc · outbound

This paper cites Neural network-based graph embed- ding for cross-platform binary code similarity detection,.

ConcernBERT: Learning Responsibilities Using Class Membership Neural network-based graph embed- ding for cross-platform binary code similarity detection,

Reference 57

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Observation 10f84ec2-a5de-40d9-bfbb-99875d14b540 · outbound

This paper cites Learning to represent programs with graphs,.

ConcernBERT: Learning Responsibilities Using Class Membership Learning to represent programs with graphs,

Reference 58

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This paper cites code2vec: Learning distributed representations of code,.

ConcernBERT: Learning Responsibilities Using Class Membership code2vec: Learning distributed representations of code,

Reference 59

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Observation ca9d25cb-af9d-4f64-beaf-20aafd1f146c · outbound

This paper cites Code Llama: Open Foundation Models for Code.

ConcernBERT: Learning Responsibilities Using Class Membership Code Llama: Open Foundation Models for Code

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-07-04T07:29:38.752099Z

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Observation 2cdd36e6-f81c-47d0-9ad1-aba1b53e2085 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

ConcernBERT: Learning Responsibilities Using Class Membership Evaluating Large Language Models Trained on Code

Reference 61

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This paper cites The stack: 3 TB of permissively licensed source code,.

ConcernBERT: Learning Responsibilities Using Class Membership The stack: 3 TB of permissively licensed source code,

Reference 62

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ConcernBERT: Learning Responsibilities Using Class Membership Sentence- bert: Sentence embeddings using siamese bert- networks,

Reference 63

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ConcernBERT: Learning Responsibilities Using Class Membership BERT: pre-training of deep bidirectional trans- formers for language understanding,

Reference 64

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ConcernBERT: Learning Responsibilities Using Class Membership Facenet: A unified embedding for face recog- nition and clustering,

Reference 65

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ConcernBERT: Learning Responsibilities Using Class Membership In Defense of the Triplet Loss for Person Re-Identification

Reference 66

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ConcernBERT: Learning Responsibilities Using Class Membership CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 67

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ConcernBERT: Learning Responsibilities Using Class Membership Multidimensional scaling by op- timizing goodness of fit to a nonmetric hypoth- esis,

Reference 68

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ConcernBERT: Learning Responsibilities Using Class Membership To- wards a metrics suite for object oriented de- sign,

Reference 69

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ConcernBERT: Learning Responsibilities Using Class Membership Software Framework for Topic Modelling with Large Corpora,

Reference 70

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ConcernBERT: Learning Responsibilities Using Class Membership Fast and eager k-medoids clustering: O(k) runtime im- provement of the pam, clara, and CLARANS algorithms,

Reference 71

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ConcernBERT: Learning Responsibilities Using Class Membership Hastie,The elements of statistical learning, second edition ed., ser

Reference 72

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Observation 0ad98d87-3910-467d-a556-323604339b0c · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Investigating the use of lex- ical information for software system cluster- ing,

Reference 73

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ConcernBERT: Learning Responsibilities Using Class Membership Hierarchical clus- tering for software architecture recovery,

Reference 74

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Observation c0d00b7a-e64a-470c-9f3e-5a423ce31729 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Software architecture recovery using integrated dependencies based on structural, semantic, and directory information,

Reference 75

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Observation aef81cf2-9065-4f0f-90e1-3113c66e586f · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Divergence measures based on the shan- non entropy,

Reference 76

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ConcernBERT: Learning Responsibilities Using Class Membership Infor- mation theoretic measures for clusterings com- parison: Variants, properties, normalization and correction for chance,

Reference 77

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ConcernBERT: Learning Responsibilities Using Class Membership An effectiveness mea- sure for software clustering algorithms,

Reference 78

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Observation a35cbbb3-4cce-4223-adc7-1b5f1533f2de · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Cluster ensembles — A knowledge reuse framework for combining mul- tiple partitions,

Reference 79

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ConcernBERT: Learning Responsibilities Using Class Membership Objective criteria for the eval- uation of clustering methods,

Reference 80

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Observation f9952888-e604-4e95-9202-fe53688773e8 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Comparing parti- tions,

Reference 81

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Observation b3b79b53-2cde-4af8-a9a6-adafe3caefde · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Dominance statistics: Ordinal anal- yses to answer ordinal questions

Reference 82

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Observation f27643ea-4269-4029-a2d7-cc79f7997a4e · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Appropriate statistics for ordi- nal level data: Should we really be using t-test and Cohen’s d for evaluating group differences on the NSSE and other surveys?

Reference 83

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Observation 66adf875-cee1-421e-98b4-a5eeb790ba37 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Wohlin, P

Reference 84

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Observation 100d6000-ab97-4783-9903-9f9515c73506 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership A complexity measure,

Reference 85

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Observation f2f3cfc4-0e7b-4c62-9834-b635a8a6fe0b · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership A metrics suite for object oriented design,

Reference 86

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Observation 619f2d68-1588-49b6-bba0-cf2a137566bc · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership A unified framework for coupling measurement in object- oriented systems,

Reference 87

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Observation 69beb23c-d3c1-40e6-acb6-faca24b53d6e · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership A unified framework for cohesion measurement in object-oriented systems,

Reference 88

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Observation d8a52bb1-5da4-40c6-a22f-b33ed8c6f4a1 · outbound

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ConcernBERT: Learning Responsibilities Using Class Membership Class cohesion metrics for software engineering: A critical re- view,

Reference 89

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