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Ayse Tosun Misirli
Ayse Tosun
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2020 – today
- 2024
- [j29]Rusen Halepmollasi
, Ayse Tosun:
Exploring the relationship between refactoring and code debt indicators. J. Softw. Evol. Process. 36(1) (2024) - [c41]Kagan Özgün, Ayse Tosun, Mehmet Tahir Sandikkaya:
A Recommender System to Detect Distributed Denial of Service Attacks with Network and Transport Layer Features. ICISSP 2024: 390-397 - [i5]Woubshet Nema Behutiye, Pilar Rodríguez, Markku Oivo, Ayse Tosun:
Analyzing the concept of technical debt in the context of agile software development: A systematic literature review. CoRR abs/2401.14882 (2024) - [i4]Beyza Eken, Samodha Pallewatta, Nguyen Khoi Tran, Ayse Tosun, Muhammad Ali Babar:
A Multivocal Review of MLOps Practices, Challenges and Open Issues. CoRR abs/2406.09737 (2024) - [i3]Oscar Dieste, Ayse Tosun, Sira Vegas
The role of slicing in test-driven development. CoRR abs/2407.13258 (2024) - 2023
- [c40]Rusen Halepmollasi, Khadija Hanifi, Ramin Fadaei Fouladi, Ayse Tosun:
A Comparison of Source Code Representation Methods to Predict Vulnerability Inducing Code Changes. ENASE 2023: 469-478 - [c39]Umutcan Karakas, Ayse Tosun:
Automated Fairness Testing with Representative Sampling. PROMISE 2023: 54-63 - 2022
- [j28]Eray Mert Kavuk, Ayse Tosun, Mucahit Cevik, Aysun Bozanta, Sibel B. Sonuc, Mehmetcan Tutuncu, Bilgin Kosucu, Ayse Basar
Order dispatching for an ultra-fast delivery service via deep reinforcement learning. Appl. Intell. 52(4): 4274-4299 (2022) - [j27]Aysun Bozanta, Mucahit Cevik, Can Kavaklioglu, Eray Mert Kavuk, Ayse Tosun, Sibel B. Sonuc, Alper Duranel, Ayse Basar
Courier routing and assignment for food delivery service using reinforcement learning. Comput. Ind. Eng. 164: 107871 (2022) - [j26]Sefa Eren Sahin
Predicting vulnerability inducing function versions using node embeddings and graph neural networks. Inf. Softw. Technol. 145: 106822 (2022) - [j25]Hadi Jahanshahi
A deep reinforcement learning approach for the meal delivery problem. Knowl. Based Syst. 243: 108489 (2022) - [j24]Iflaah Salman
What Leads to a Confirmatory or Disconfirmatory Behavior of Software Testers? IEEE Trans. Software Eng. 48(4): 1351-1368 (2022) - [c38]Tugce Coskun, Rusen Halepmollasi
Profiling developers to predict vulnerable code changes. PROMISE 2022: 32-41 - 2021
- [j23]Adrian Santos, Sira Vegas
A family of experiments on test-driven development. Empir. Softw. Eng. 26(3): 42 (2021) - [j22]Beyza Eken
Investigating the performance of personalized models for software defect prediction. J. Syst. Softw. 181: 111038 (2021) - [j21]Beyza Eken
Deployment of a change-level software defect prediction solution into an industrial setting. J. Softw. Evol. Process. 33(11) (2021) - [j20]Beyza Eken
An empirical study on the effect of community smells on bug prediction. Softw. Qual. J. 29(1): 159-194 (2021) - [j19]Ayse Tosun
Investigating the Impact of Development Task on External Quality in Test-Driven Development: An Industry Experiment. IEEE Trans. Software Eng. 47(11): 2438-2456 (2021) - [c37]Anil Holat, Ayse Tosun:
Predicting Requirements Volatility: An Industry Case Study. SEED/QuASoQ@APSEC 2021: 51-59 - [c36]Derya Yeliz Ulutas, Ayse Tosun:
A Condition Coverage-Based Black Hole Inspired Meta-Heuristic for Test Data Generation. SEED/QuASoQ@APSEC 2021: 70-78 - [i2]Hadi Jahanshahi, Aysun Bozanta, Mucahit Cevik, Eray Mert Kavuk, Ayse Tosun, Sibel B. Sonuc, Bilgin Kosucu, Ayse Basar:
A Deep Reinforcement Learning Approach for the Meal Delivery Problem. CoRR abs/2104.12000 (2021) - 2020
- [j18]Ayse Tosun
Guest Editorial: Special Issue on Predictive Models and Data Analytics in Software Engineering. Empir. Softw. Eng. 25(2): 1533-1535 (2020) - [c35]Eray Mert Kavuk, Ayse Tosun:
Predicting Stack Overflow Question Tags: A Multi-Class, Multi-Label Classification. ICSE (Workshops) 2020: 489-493 - [c34]Ege Apak, Ayse Tosun:
Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding. ICSE (Workshops) 2020: 533-536 - [c33]Kazim Kivanç Eren, Can Özbey, Beyza Eken
Customer requests matter: early stage software effort estimation using k-grams. SAC 2020: 1540-1547 - [i1]Adrian Santos, Sira Vegas, Oscar Dieste, Fernando Uyaguari Uyaguari, Ayse Tosun, Davide Fucci, Burak Turhan, Giuseppe Scanniello, Simone Romano, Itir Karac, Marco Kuhrmann, Vladimir Mandic
A Family of Experiments on Test-Driven Development. CoRR abs/2011.11942 (2020)
2010 – 2019
- 2019
- [j17]Asli Sari
A systematic literature review on crowdsourcing in software engineering. J. Syst. Softw. 153: 200-219 (2019) - [c32]Sefa Eren Sahin, Ayse Tosun
A Conceptual Replication on Predicting the Severity of Software Vulnerabilities. EASE 2019: 244-250 - [c31]Beyza Eken
Predicting Defects with Latent and Semantic Features from Commit Logs in an Industrial Setting. ASE Workshops 2019: 98-105 - [c30]Sefa Eren Sahin, Kubilay Karpat, Ayse Tosun
Predicting Popularity of Open Source Projects Using Recurrent Neural Networks. OSS 2019: 80-90 - 2018
- [c29]Emre Sahal, Ayse Tosun
Identifying bug-inducing changes for code additions. ESEM 2018: 57:1-57:2 - [c28]Oscar Dieste
Empirical evaluation of the effects of experience on code quality and programmer productivity: an exploratory study. ICSSP 2018: 111-112 - [c27]Ayse Tosun
On the effectiveness of unit tests in test-driven development. ICSSP 2018: 113-122 - [c26]Alim Ozdemir, Ayse Tosun
Lightweight source code monitoring with Triggr. ASE 2018: 864-867 - [c25]Seldag Ozcan Kini, Ayse Tosun
[Research Paper] Periodic Developer Metrics in Software Defect Prediction. SCAM 2018: 72-81 - [e1]Burak Turhan
Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2018, Oulu, Finland, October 10, 2018. ACM 2018 [contents] - 2017
- [j16]Ayse Tosun
Erratum to: Studying high impact fix-inducing changes. Empir. Softw. Eng. 22(2): 848 (2017) - [j15]Oscar Dieste
Empirical evaluation of the effects of experience on code quality and programmer productivity: an exploratory study. Empir. Softw. Eng. 22(5): 2457-2542 (2017) - [j14]Ayse Tosun
An industry experiment on the effects of test-driven development on external quality and productivity. Empir. Softw. Eng. 22(6): 2763-2805 (2017) - [j13]Woubshet Nema Behutiye
Analyzing the concept of technical debt in the context of agile software development: A systematic literature review. Inf. Softw. Technol. 82: 139-158 (2017) - [j12]Ayse Tosun
A systematic literature review on the applications of Bayesian networks to predict software quality. Softw. Qual. J. 25(1): 273-305 (2017) - [c24]Ayse Tosun
Predicting defects using test execution logs in an industrial setting. ICSE (Companion Volume) 2017: 294-296 - 2016
- [j11]Ayse Tosun Misirli
Studying high impact fix-inducing changes. Empir. Softw. Eng. 21(2): 605-641 (2016) - [c23]Shirin Akbarinasaji, Behjat Soltanifar, Bora Çaglayan, Ayse Basar Bener
A metric suite proposal for logical dependency. WETSoM@ICSE 2016: 57-63 - [p4]Ayse Bener
A success story in applying data science in practice. Perspectives on Data Science for Software Engineering 2016: 85-90 - [p3]Ayse Bener
If it is software engineering, it is (probably) a Bayesian factor. Perspectives on Data Science for Software Engineering 2016: 187-191 - 2015
- [j10]Ayse Tosun Misirli
Factors Affecting Team Motivation: A Survey of Finnish Software Engineers. Int. J. Inf. Syst. Model. Des. 6(3): 1-26 (2015) - [j9]Davide Fucci
Towards an operationalization of test-driven development skills: An industrial empirical study. Inf. Softw. Technol. 68: 82-97 (2015) - [j8]Bora Caglayan, Ayse Tosun Misirli
Predicting defective modules in different test phases. Softw. Qual. J. 23(2): 205-227 (2015) - [c22]Iflaah Salman, Ayse Tosun Misirli
Are Students Representatives of Professionals in Software Engineering Experiments? ICSE (1) 2015: 666-676 - [p2]Ayse Bener
Lessons Learned from Software Analytics in Practice. The Art and Science of Analyzing Software Data 2015: 453-489 - 2014
- [j7]Ayse Tosun Misirli
Bayesian Networks For Evidence-Based Decision-Making in Software Engineering. IEEE Trans. Software Eng. 40(6): 533-554 (2014) - [c21]Ayse Tosun Misirli
A mapping study on bayesian networks for software quality prediction. RAISE 2014: 7-11 - [c20]Ayse Tosun Misirli
Topic selection in industry experiments. CESI 2014: 25-30 - [c19]Ayse Tosun Misirli
A survey on project factors that motivate Finnish software engineers. RCIS 2014: 1-9 - [p1]Ayse Tosun Misirli
Field Studies - A Methodology for Construction and Evaluation of Recommendation Systems in Software Engineering. Recommendation Systems in Software Engineering 2014: 329-355 - 2013
- [j6]Burak Turhan
Empirical evaluation of the effects of mixed project data on learning defect predictors. Inf. Softw. Technol. 55(6): 1101-1118 (2013) - [j5]Ayse Tosun Misirli
A Retrospective Study of Software Analytics Projects: In-Depth Interviews with Practitioners. IEEE Softw. 30(5): 54-61 (2013) - 2012
- [b1]Ayse Tosun Misirli:
Modelling software reliability using hybrid Bayesian networks (Yazılım güvenilirliğinin hibrid Bayes ağları kullanılarak modellenmesi). Boğaziçi University, Turkey, 2012 - [c18]Gul Calikli, Ayse Bener, Bora Caglayan, Ayse Tosun Misirli:
Modeling Human Aspects to Enhance Software Quality Management. ICIS 2012 - [c17]Bora Caglayan, Ayse Tosun Misirli
Factors characterizing reopened issues: a case study. PROMISE 2012: 1-10 - [c16]Bora Caglayan, Ayse Tosun Misirli
Dione: an integrated measurement and defect prediction solution. SIGSOFT FSE 2012: 20 - 2011
- [j4]Ayse Tosun Misirli
AI-Based Software Defect Predictors: Applications and Benefits in a Case Study. AI Mag. 32(2): 57-68 (2011) - [j3]Ayse Tosun Misirli
An industrial case study of classifier ensembles for locating software defects. Softw. Qual. J. 19(3): 515-536 (2011) - [c15]Burak Turhan
Empirical Evaluation of Mixed-Project Defect Prediction Models. EUROMICRO-SEAA 2011: 396-403 - [c14]Ekrem Kocaguneli, Ayse Tosun Misirli
Experiences on Developer Participation and Effort Estimation. EUROMICRO-SEAA 2011: 419-422 - [c13]Ayse Tosun Misirli
Different strokes for different folks: a case study on software metrics for different defect categories. WETSoM 2011: 45-51 - [c12]Ayse Tosun Misirli
An explanatory analysis on eclipse beta-release bugs through in-process metrics. WoSQ@ESEC/FSE 2011: 26-33 - 2010
- [j2]Ayse Tosun
Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry. Inf. Softw. Technol. 52(11): 1242-1257 (2010) - [c11]Ekrem Kocaguneli, Ayse Tosun
AI-Based Models for Software Effort Estimation. EUROMICRO-SEAA 2010: 323-326 - [c10]Ayse Tosun, Ayse Basar Bener, Resat Kale:
AI-Based Software Defect Predictors: Applications and Benefits in a Case Study. IAAI 2010: 1748-1755 - [c9]Bora Caglayan, Ayse Tosun
Usage of multiple prediction models based on defect categories. PROMISE 2010: 8
2000 – 2009
- 2009
- [j1]Ayse Tosun
Feature weighting heuristics for analogy-based effort estimation models. Expert Syst. Appl. 36(7): 10325-10333 (2009) - [c8]Ayse Tosun
Reducing false alarms in software defect prediction by decision threshold optimization. ESEM 2009: 477-480 - [c7]Ayse Tosun
Implementation of a Software Quality Improvement Project in an SME: A Before and After Comparison. EUROMICRO-SEAA 2009: 203-209 - [c6]Gul Calikli
The effect of granularity level on software defect prediction. ISCIS 2009: 531-536 - [c5]Ayse Tosun
Validation of network measures as indicators of defective modules in software systems. PROMISE 2009: 5 - [c4]Ayse Tosun
Practical considerations in deploying AI for defect prediction: a case study within the Turkish telecommunication industry. PROMISE 2009: 11 - [c3]Ayse Tosun, Ayse Basar Bener, Ekrem Kocaguneli:
BITS: Issue Tracking and Project Management Tool in Healthcare Software Development. SEKE 2009: 526-529 - [c2]Ekrem Kocaguneli, Ayse Tosun, Ayse Basar Bener, Burak Turhan, Bora Caglayan:
Prest: An Intelligent Software Metrics Extraction, Analysis and Defect Prediction Tool. SEKE 2009: 637-642 - 2008
- [c1]Ayse Tosun
Ensemble of software defect predictors: a case study. ESEM 2008: 318-320
Coauthor Index
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