| 2 | Defect prediction from static code features: current results, limitations, new approaches | Automated Software Engineering | 2010 | 218 |
| 3 | An investigation on the feasibility of cross-project defect prediction | Automated Software Engineering | 2011 | 178 |
| 4 | Sample-based software defect prediction with active and semi-supervised learning | Automated Software Engineering | 2011 | 101 |
| 5 | Multiple kernel ensemble learning for software defect prediction | Automated Software Engineering | 2015 | 81 |
| 6 | Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction | Automated Software Engineering | 2017 | 80 |
| 7 | Label propagation based semi-supervised learning for software defect prediction | Automated Software Engineering | 2016 | 79 |
| 8 | Maintainability defects detection and correction: a multi-objective approach | Automated Software Engineering | 2012 | 71 |
| 9 | A journey to highly dynamic, self-adaptive service-based applications | Automated Software Engineering | 2008 | 62 |
| 10 | An approach to prioritize code smells for refactoring | Automated Software Engineering | 2014 | 57 |
| 11 | Prioritizing test cases with string distances | Automated Software Engineering | 2011 | 54 |
| 12 | Model checking agent programming languages | Automated Software Engineering | 2011 | 53 |
| 13 | GUITAR: an innovative tool for automated testing of GUI-driven software | Automated Software Engineering | 2013 | 51 |
| 14 | Efficient deep-reinforcement learning aware resource allocation in SDN-enabled fog paradigm | Automated Software Engineering | 2022 | 51 |
| 15 | Rewriting-Based Techniques for Runtime Verification | Automated Software Engineering | 2005 | 50 |
| 16 | Data cleaning and machine learning: a systematic literature review | Automated Software Engineering | 2024 | 49 |
| 17 | An NLP approach for cross-domain ambiguity detection in requirements engineering | Automated Software Engineering | 2019 | 48 |
| 20 | TestEra: Specification-Based Testing of Java Programs Using SAT | Automated Software Engineering | 2004 | 42 |
| 21 | The DOPLER meta-tool for decision-oriented variability modeling: a multiple case study | Automated Software Engineering | 2010 | 42 |
| 24 | On the Systematic Analysis of Natural Language Requirements with CIRCE | Automated Software Engineering | 2006 | 41 |
| 25 | A survey on search-based model-driven engineering | Automated Software Engineering | 2017 | 41 |
| 26 | Heterogeneous defect prediction with two-stage ensemble learning | Automated Software Engineering | 2019 | 41 |
| 27 | Finding conclusion stability for selecting the best effort predictor in software effort estimation | Automated Software Engineering | 2012 | 40 |
| 28 | AbstFinder, A Prototype Natural Language Text Abstraction Finder for Use in Requirements Elicitation | Automated Software Engineering | 1997 | 39 |
| 29 | Understanding machine learning software defect predictions | Automated Software Engineering | 2020 | 38 |
| 30 | Future of software development with generative AI | Automated Software Engineering | 2024 | 38 |
| 31 | Pattern matching for clone and concept detection | Automated Software Engineering | 1996 | 37 |
| 32 | Symbolic PathFinder: integrating symbolic execution with model checking for Java bytecode analysis | Automated Software Engineering | 2013 | 37 |
| 33 | Practical verification of decision-making in agent-based autonomous systems | Automated Software Engineering | 2014 | 35 |
| 34 | A Novel Technique for Accelerating Live Migration in Cloud Computing | Automated Software Engineering | 2022 | 35 |
| 35 | How to certify machine learning based safety-critical systems? A systematic literature review | Automated Software Engineering | 2022 | 35 |
| 36 | Type safety for feature-oriented product lines | Automated Software Engineering | 2010 | 32 |
| 37 | Prediction of software fault-prone classes using ensemble random forest with adaptive synthetic sampling algorithm | Automated Software Engineering | 2021 | 32 |
| 38 | Automatic, high accuracy prediction of reopened bugs | Automated Software Engineering | 2014 | 31 |
| 39 | Convolutional neural network for stock trading using technical indicators | Automated Software Engineering | 2022 | 31 |
| 40 | Automated verification of model transformations based on visual contracts | Automated Software Engineering | 2012 | 30 |
| 41 | Runtime recovery and manipulation of software architecture of component-based systems | Automated Software Engineering | 2006 | 29 |
| 42 | JDiff: A differencing technique and tool for object-oriented programs | Automated Software Engineering | 2006 | 29 |
| 44 | Test input reduction for result inspection to facilitate fault localization | Automated Software Engineering | 2009 | 27 |
| 45 | Improving code completion with program history | Automated Software Engineering | 2010 | 27 |
| 46 | An Architecture based on interactive optimization and machine learning applied to the next release problem | Automated Software Engineering | 2016 | 27 |
| 47 | Deep learning approach for intrusion detection in IoT-multi cloud environment | Automated Software Engineering | 2021 | 27 |
| 48 | Improving the prediction of continuous integration build failures using deep learning | Automated Software Engineering | 2022 | 26 |
| 49 | Graphical scenarios for specifying temporal properties: an automated approach | Automated Software Engineering | 2007 | 25 |
| 50 | An exploratory study for software change prediction in object-oriented systems using hybridized techniques | Automated Software Engineering | 2016 | 25 |
