Empirical Software Engineering

Papers
(The TQCC of Empirical Software Engineering is 10. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-08-01 to 2026-08-01.)
ArticleCitations
Introduction to the special issue on program comprehension119
Consensus task interaction trace recommender to guide developers’ software navigation107
Underproduction analysis of open source software101
The human experience of comprehending source code in virtual reality92
Security by documentation? characterizing GitHub SECURITY.md policy and their adoption in Python libraries81
The design space of lockfiles across package managers79
(In)Security of mobile apps in developing countries: a systematic literature review64
Optimal priority assignment for real-time systems: a coevolution-based approach59
Bugs in machine learning-based systems: a faultload benchmark58
Fuzzing-based mutation testing of C/C++ software in cyber-physical systems56
Evaluating software user feedback classifier performance on unseen apps, datasets, and metadata53
Does the first response matter for future contributions? A study of first contributions53
Evaluating few-shot and contrastive learning methods for code clone detection51
Can static analysis tools find more defects?43
Understanding the characteristics and the role of visual issue reports42
On the adoption and effects of source code reuse on defect proneness and maintenance effort41
More than React: Investigating the Role of Emoji Reaction in GitHub Pull Requests40
Seeing the invisible: test prioritization for object detection system40
Mitigating omitted variable bias in empirical software engineering40
An empirical study on the effectiveness of large language models for SATD identification and classification39
TestEvoViz: visualizing genetically-based test coverage evolution38
Shaky structures: The wobbly world of causal graphs in software analytics38
Toward effective secure code reviews: an empirical study of security-related coding weaknesses38
A study of documentation for software architecture36
Developers’ perception matters: machine learning to detect developer-sensitive smells36
Cross-status communication and project outcomes in OSS development34
Maintaining shared understanding of non-functional requirements in small companies using continuous software engineering33
Output format biases in the evaluation of large language models for code translation33
What causes exceptions in machine learning applications? Mining machine learning-related stack traces on Stack Overflow32
Towards cost-benefit evaluation for continuous software engineering activities32
Testing the past: can we still run tests in past snapshots for Java projects?32
Collaboration failure analysis in cyber-physical system-of-systems using context fuzzy clustering32
The impact of class imbalance techniques on crashing fault residence prediction models32
Smells in system user interactive tests29
Analyzing and mitigating (with LLMs) the security misconfigurations of Helm charts from Artifact Hub29
The transformative potential of AI in software engineering: a case study on LeetCode and ChatGPT29
Aspect-centric vulnerability understanding via semantics-aware commit representation learning28
Evaluating the impact of flaky simulators on testing autonomous driving systems28
On the emergence of testing strategies: A socio-technical grounded theory28
Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability27
Automated test generation for Scratch programs27
The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts27
Deep learning based identification of inconsistent method names: How far are we?26
App review driven collaborative bug finding26
Automatic prediction of rejected edits in Stack Overflow25
Deep learning techniques to detect cybersecurity attacks: a systematic mapping study25
Real world projects, real faults: evaluating spectrum based fault localization techniques on Python projects24
A fine-grained taxonomy of code review feedback in TypeScript projects24
An empirical study of untangling patterns of two-class dependency cycles24
The impact of the COVID-19 pandemic on women’s contribution to public code24
The effect of stereotypes on perceived competence of indigenous software practitioners: a study of dress style in professional photos24
BTLink : automatic link recovery between issues and commits based on pre-trained BERT model24
Quantifying adoption: A SEM study of quantum software technology in software development23
How far are app secrets from being stolen? a case study on android23
AI support for data scientists: An empirical study on workflow and alternative code recommendations23
Indentation and reading time: a randomized control trial on the differences between generated indented and non-indented if-statements22
A configurable method for benchmarking scalability of cloud-native applications22
An empirical evaluation of a novel domain-specific language – modelling vehicle routing problems with Athos21
A grounded theory of community package maintenance organizations21
Static detection of equivalent mutants in real-time model-based mutation testing21
How far are we with automated machine learning? characterization and challenges of AutoML toolkits21
JNFuzz-Droid: a lightweight fuzzing and taint analysis framework for native code of Android applications21
A Comprehensive Study of the Lifecycle of Dormant npm Packages21
Advantages and disadvantages of (dedicated) model transformation languages20
Scalable hierarchical protocol format inference via feature-heuristic message delimiter20
Code reviews in open source projects : how do gender biases affect participation and outcomes?20
Why android app testing falls short: empirical insights from open-source projects and a practitioner survey19
A large-scale empirical study of commit message generation: models, datasets and evaluation19
On combining commit grouping and build skip prediction to reduce redundant continuous integration activity19
Balancing usefulness and naturalness: an LLM-based curation pipeline for code review comments19
How students use generative AI for software testing: An observational study19
An empirical study of the impact of log parsers on the performance of log-based anomaly detection19
Experimental comparison of features, analyses, and classifiers for Android malware detection19
The well-being of software engineers: a systematic literature review and a theory19
Understanding practitioners’ reasoning and requirements for efficient tool support in technical debt management19
Engineering recommender systems for modelling languages: concept, tool and evaluation18
ContractFull: a rapid and comprehensive static analysis tool for Ethereum smart contracts18
A metrics-based approach for selecting among various refactoring candidates18
Local software buildability across Java versions18
Securing dependencies: A comprehensive study of Dependabot’s impact on vulnerability mitigation18
Validation of an analyzability model for quantum software: a family of experiments17
Identifying Quality Indicators in Student Self-Reflections in Software Engineering17
Software product line testing: a systematic literature review17
An empirical study on the potential of word embedding techniques in bug report management tasks17
From values to adoption: on the role of individual cultural values on fairness toolkit adoption in software development17
Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction17
Lightweight dynamic build batching algorithms for continuous integration17
Securing LLM-in-the-loop software for empirical study of risks, mitigations, and utility trade-offs in a safety-critical case17
Revisiting code readability improvement with LLMs: A critical assessment of fine-tuned models17
LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction17
Software testing in the machine learning era16
Tools and benchmarks evolve: what is their impact on parameter tuning in SBSE experiments?16
Patterns of multi-container composition for service orchestration with Docker Compose16
Enhanced SQL error messages facilitate faster error fixing16
Language usage analysis for EMF metamodels on GitHub16
What really changes when developers intend to improve their source code: a commit-level study of static metric value and static analysis warning changes16
RAG-Driven multiple assertions generation with large language models16
Can the configuration of static analyses make resolving security vulnerabilities more effective? - A user study15
Semantic matching in GUI test reuse15
What kinds of contracts do ML APIs need?15
Common challenges of deep reinforcement learning applications development: an empirical study15
An investigation of online and offline learning models for online Just-in-Time Software Defect Prediction15
DRECT: A search-based developer recommendation approach for software crowdsourcing platforms15
Mastering uncertainty in performance estimations of configurable software systems15
Test smells 20 years later: detectability, validity, and reliability15
On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code15
Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?15
Preface to the Special Issue on Security Testing for Complex Software Systems Special Issue 1239 Editorial15
OpTrans: enhancing binary code similarity detection with function inlining re-optimization15
When less is more: on the value of “co-training” for semi-supervised software defect predictors15
Comparing effectiveness and efficiency of Interactive Application Security Testing (IAST) and Runtime Application Self-Protection (RASP) tools in a large java-based system15
Less is more: balancing models performance and complexity for software defects prediction15
Empirical benchmarking of large language models for data science coding: a multidimensional evaluation15
SmartFast: an accurate and robust formal analysis tool for Ethereum smart contracts14
Semantically-enhanced topic recommendation systems for software projects14
Toward granular search-based automatic unit test case generation14
Challenges and practices of deep learning model reengineering: A case study on computer vision14
Program transformation landscapes for automated program modification using Gin14
Correction to: Examining ownership models in software teams14
Classifier or prompt: A case study on legal requirements traceability14
A zero-shot framework for cross-project vulnerability detection in source code14
An empirical study of testing practices in open source AI agent frameworks and agentic applications14
Exploring the black box: analysing explainable AI challenges and best practices through stack exchange discussions14
Test schedule generation for acceptance testing of mission-critical satellite systems14
Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment14
Meta-enhanced code: leveraging structural and functional features for precise cross-modal code search14
Which design decisions in AI-enabled mobile applications contribute to greener AI?14
An exploratory study on fine-tuning large language models for secure code generation13
DDImage: an image reduction based approach for automatically explaining black-box classifiers13
Towards understanding the challenges of bug localization in deep learning systems13
Identifying performance-sensitive configurations in software systems with LLM-based agents13
Implicit security requirements classification with large language models using the OWASP application security verification standard: a shift-left approach13
Static analysis driven enhancements for comprehension in machine learning notebooks13
Prioritizing test cases for deep learning-based video classifiers13
KPIRoot+: An efficient integrated framework for anomaly detection and root cause analysis in large-scale cloud systems13
A fine-grained evaluation of mutation operators to boost mutation testing for deep learning systems13
What have we learned? A conceptual framework on New Zealand software professionals and companies’ response to COVID-1913
How challenging it is to identify real code authors: an empirical study13
Measuring SES-related traits relating to technology usage: Two validated surveys13
Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP13
Demystifying API misuses in deep learning applications13
A controlled experiment on the impact of microtasking on programming13
A multi-model framework for semantically enhancing detection of quality-related bug report descriptions13
Automated detection of algorithm debt in deep learning frameworks: an empirical study12
Unveiling overlooked performance variance in serverless computing12
When uncertainty leads to unsafety: Empirical insights into the role of uncertainty in unmanned aerial vehicle safety12
On the spread and evolution of dead methods in Java desktop applications: an exploratory study12
Towards automatic labeling of exception handling bugs: A case study of 10 years bug-fixing in Apache Hadoop12
Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software Systems with the Brazilian General Data Protection Law12
CyberSAGE: The cyber security argument graph evaluation tool12
Explainable automated debugging via large language model-driven scientific debugging11
Detecting data manipulation errors in android applications using scene-guided exploration11
CMF-Vul: Advancing automated vulnerability detection via contrastive multimodal fusion and challenge-driven representation learning11
Styler: learning formatting conventions to repair Checkstyle violations11
Modeling function-level interactions for file-level bug localization11
Learning to Predict Code Review Completion Time In Modern Code Review11
Transformer-based code model with compressed hierarchy representation11
ComPass: Contrastive Learning for Automated Patch Correctness Assessment in Program Repair11
Fixing Dockerfile smells: an empirical study11
Cross-project defect prediction via semantic and syntactic encoding11
APR4Vul: an empirical study of automatic program repair techniques on real-world Java vulnerabilities11
Predicting merge conflicts considering social and technical assets11
Refactoring practices in the context of data-intensive systems11
Studying differentiated code to support smart contract update11
On detection latencies of network intrusion detectors – discussion and application11
Seeing confusion through a new lens: on the impact of atoms of confusion on novices’ code comprehension11
Investigating cross-market android apps: Security, protection, and components10
Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow10
Towards understanding quality challenges of the federated learning for neural networks: a first look from the lens of robustness10
Understanding refactorings in Elixir functional language10
Web element relocalization in evolving web applications: A comparative analysis and extension study10
Studying the characteristics of AIOps projects on GitHub10
Story points changes in agile iterative development10
Hyperfuzzing: black-box security hypertesting with a grey-box fuzzer10
Understanding and effectively mitigating code review anxiety10
Detecting API compatibility issues of android applications based on screen transition graphs10
Model vs system level testing of autonomous driving systems: a replication and extension study10
Automatic bi-modal question title generation for Stack Overflow with prompt learning10
Software selection in large-scale software engineering: A model and criteria based on interactive rapid reviews10
Empirically evaluating flaky test detection techniques combining test case rerunning and machine learning models10
An empirical study on developers’ shared conversations with ChatGPT in GitHub pull requests and issues10
Toward a theory on programmer’s block inspired by writer’s block10
Assessing the exposure of software changes10
A qualitative study on refactorings induced by code review10
Correction to: Why do companies create and how do they succeed with a vendor-led open source foundation10
From guidelines to practice: assessing Android app developer compliance with google’s security recommendations10
Multi-granular software annotation using file-level weak labelling10
Navigating fairness: practitioners’ understanding, challenges, and strategies in AI/ML development10
Large language models in model-driven engineering: a systematic mapping study10
0.066582918167114