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“Bad smells” in software analytics papers
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1097-0258(20000515)19:9<1141::aid-sim479>3.0.co;2-fopenalex-snapshot
Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians
inferredcites →https://doi.org/10.1007/978-1-84800-044-5openalex-snapshot
Guide to Advanced Empirical Software Engineering
inferredcites →https://doi.org/10.1007/978-1-84800-044-5_12openalex-snapshot
Building Theories in Software Engineering
inferredcites →https://doi.org/10.1007/978-3-642-29044-2openalex-snapshot
Experimentation in Software Engineering
inferredcites →https://doi.org/10.1007/s10664-008-9102-8openalex-snapshot
Guidelines for conducting and reporting case study research in software engineering
inferredcites →https://doi.org/10.1007/s10664-010-9139-3openalex-snapshot
Reporting computing projects through structured abstracts: a quasi-experiment
inferredcites →https://doi.org/10.1007/s10664-010-9146-4openalex-snapshot
A method for evaluating rigor and industrial relevance of technology evaluations
inferredcites →https://doi.org/10.1007/s10664-011-9193-5openalex-snapshot
Special issue on repeatable results in software engineering prediction
inferredcites →https://doi.org/10.1007/s10664-013-9279-3openalex-snapshot
A practical guide to controlled experiments of software engineering tools with human participants
inferredcites →https://doi.org/10.1007/s10664-016-9437-5openalex-snapshot
Robust Statistical Methods for Empirical Software Engineering
inferredcites →https://doi.org/10.1016/0198-9715(90)90050-4openalex-snapshot
Statistical power analysis for the behavioral sciences
inferredcites →https://doi.org/10.1016/c2010-0-67044-1openalex-snapshot
Introduction to Robust Estimation and Hypothesis Testing
inferredcites →https://doi.org/10.1016/j.infsof.2005.08.009openalex-snapshot
A systematic review of statistical power in software engineering experiments
inferredcites →https://doi.org/10.1016/j.infsof.2007.02.015openalex-snapshot
A systematic review of effect size in software engineering experiments
inferredcites →https://doi.org/10.1016/j.infsof.2011.01.004openalex-snapshot
Research synthesis in software engineering: A tertiary study
inferredcites →https://doi.org/10.1016/j.infsof.2015.03.007openalex-snapshot
Guidelines for conducting systematic mapping studies in software engineering: An update
inferredcites →https://doi.org/10.1016/j.infsof.2016.04.017openalex-snapshot
Tuning for software analytics: Is it really necessary?
inferredcites →https://doi.org/10.1016/j.infsof.2018.02.005openalex-snapshot
What is wrong with topic modeling? And how to fix it using search-based software engineering
inferredcites →https://doi.org/10.1016/j.jss.2015.03.065openalex-snapshot
Incorrect results in software engineering experiments: How to improve research practices
inferredcites →https://doi.org/10.1016/s0895-4356(00)00314-0openalex-snapshot
Adjusting for multiple testing—when and how?
inferredcites →https://doi.org/10.1017/cbo9780511761676openalex-snapshot
The Essential Guide to Effect Sizes
inferredcites →https://doi.org/10.1023/a:1008202821328openalex-snapshot
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
inferredcites →https://doi.org/10.1023/a:1022631118932openalex-snapshot
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
inferredcites →https://doi.org/10.1037//0003-066x.55.8.963openalex-snapshot
Increasing statistical power without increasing sample size.
inferredcites →https://doi.org/10.1037//0033-2909.97.1.119openalex-snapshot
Comparison of the power of the paired samples t test to that of Wilcoxon's signed-ranks test under various population shapes.
inferredcites →https://doi.org/10.1037/0003-066x.63.7.591openalex-snapshot
Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.
inferredcites →https://doi.org/10.1037/1089-2680.2.2.175openalex-snapshot
Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
inferredcites →https://doi.org/10.1038/nrn3475openalex-snapshot
Power failure: why small sample size undermines the reliability of neuroscience
inferredcites →https://doi.org/10.1038/s41562-016-0021openalex-snapshot
A manifesto for reproducible science
inferredcites →https://doi.org/10.1046/j.1365-2532.1997.09191.xopenalex-snapshot
The value of structured abstracts in information retrieval from MEDLINE
inferredcites →https://doi.org/10.1049/iet-sen:20070044openalex-snapshot
Length and readability of structured software engineering abstracts
inferredcites →https://doi.org/10.1049/sej.1993.0002openalex-snapshot
Shotgun correlations in software measures
inferredcites →https://doi.org/10.1080/00220973.1993.10806591openalex-snapshot
The Case Against Statistical Significance Testing, Revisited
inferredcites →https://doi.org/10.1080/00220979809598344openalex-snapshot
Invalidation of Parametric and Nonparametric Statistical Tests by Concurrent Violation of Two Assumptions
inferredcites →https://doi.org/10.1080/00401706.1989.10488618openalex-snapshot
Statistical Power Analysis for the Behavioral Sciences
inferredcites →https://doi.org/10.1080/09332480.2005.10722754openalex-snapshot
Why Most Published Research Findings Are False
inferredcites →https://doi.org/10.1093/nar/gky780openalex-snapshot
Power, false discovery rate and Winner’s Curse in eQTL studies
inferredcites →https://doi.org/10.1098/rsos.140216openalex-snapshot
An investigation of the false discovery rate and the misinterpretation of p -values
inferredcites →https://doi.org/10.1098/rsos.160384openalex-snapshot
The natural selection of bad science
inferredcites →https://doi.org/10.1109/apsec.2013.26openalex-snapshot
Can We Trust Our Results? A Mapping Study on Data Quality
inferredcites →https://doi.org/10.1109/ase.2015.45openalex-snapshot
Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T)
inferredcites →https://doi.org/10.1109/concapan.2017.8278488openalex-snapshot
Refactoring improving the design of existing code
inferredcites →https://doi.org/10.1109/gtse.2013.6613863openalex-snapshot
Uncovering theories in software engineering
inferredcites →https://doi.org/10.1109/icse-c.2017.51openalex-snapshot
Writing Good Software Engineering Research Papers: Revisited
inferredcites →https://doi.org/10.1109/icse.2001openalex-snapshot
Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001
inferredcites →https://doi.org/10.1109/icse.2003.1201262openalex-snapshot
Writing good software engineering research papers
inferredcites →https://doi.org/10.1109/icse.2015.91openalex-snapshot
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
inferredcites →https://doi.org/10.1109/icsme.2017.51openalex-snapshot
Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction
inferredcites →https://doi.org/10.1109/ms.2005.151openalex-snapshot
Finding the Right Data for Software Cost Modeling
inferredcites →https://doi.org/10.1109/ms.2012.127openalex-snapshot
Where's the Theory for Software Engineering?
inferredcites →https://doi.org/10.1109/raise.2013.6615200openalex-snapshot
Pareto-optimal search-based software engineering (POSBSE): A literature survey
inferredcites →https://doi.org/10.1109/tevc.2007.892759openalex-snapshot
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
inferredcites →https://doi.org/10.1109/tevc.2013.2281535openalex-snapshot
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
inferredcites →https://doi.org/10.1109/tkde.2003.1245283openalex-snapshot
Benchmarking attribute selection techniques for discrete class data mining
inferredcites →https://doi.org/10.1109/tse.2002.1027796openalex-snapshot
Preliminary guidelines for empirical research in software engineering
inferredcites →https://doi.org/10.1109/tse.2007.256941openalex-snapshot
Data Mining Static Code Attributes to Learn Defect Predictors
inferredcites →https://doi.org/10.1109/tse.2008.35openalex-snapshot
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
inferredcites →https://doi.org/10.1109/tse.2012.45openalex-snapshot
Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm
inferredcites →https://doi.org/10.1109/tse.2013.11openalex-snapshot
Data Quality: Some Comments on the NASA Software Defect Datasets
inferredcites →https://doi.org/10.1109/tse.2014.2322358openalex-snapshot
Researcher Bias: The Use of Machine Learning in Software Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2018.2790925openalex-snapshot
“Sampling” as a Baseline Optimizer for Search-Based Software Engineering
inferredcites →https://doi.org/10.1109/tse.2018.2794977openalex-snapshot
The Impact of Automated Parameter Optimization on Defect Prediction Models
inferredcites →https://doi.org/10.1111/j.1467-985x.2014.12096_1.xopenalex-snapshot
Bayesian Data Analysis
inferredcites →https://doi.org/10.1126/science.aac4716openalex-snapshot
Estimating the reproducibility of psychological science
inferredcites →https://doi.org/10.1126/science.aal3618openalex-snapshot
Measurement error and the replication crisis
inferredcites →https://doi.org/10.1126/scitranslmed.aaf5027openalex-snapshot
What does research reproducibility mean?
inferredcites →https://doi.org/10.1145/1062455openalex-snapshot
Proceedings of the 27th international conference on Software engineering - ICSE '05
inferredcites →https://doi.org/10.1145/1370788.1370799openalex-snapshot
Data sets and data quality in software engineering
inferredcites →https://doi.org/10.1145/1414004.1414034openalex-snapshot
Strength of evidence in systematic reviews in software engineering
inferredcites →https://doi.org/10.1145/1985793.1985795openalex-snapshot
A practical guide for using statistical tests to assess randomized algorithms in software engineering
inferredcites →https://doi.org/10.1145/2460999.2461024openalex-snapshot
Data quality in empirical software engineering
inferredcites →https://doi.org/10.1145/2568225.2568233openalex-snapshot
Analyze this! 145 questions for data scientists in software engineering
inferredcites →https://doi.org/10.1145/2738037openalex-snapshot
A Baseline Model for Software Effort Estimation
inferredcites →https://doi.org/10.1145/2786805.2786809openalex-snapshot
How practitioners perceive the relevance of software engineering research
inferredcites →https://doi.org/10.1145/2884781.2884812openalex-snapshot
Belief & evidence in empirical software engineering
inferredcites →https://doi.org/10.1145/2884781.2884833openalex-snapshot
Grounded theory in software engineering research
inferredcites →https://doi.org/10.1145/2884781.2884857openalex-snapshot
Automated parameter optimization of classification techniques for defect prediction models
inferredcites →https://doi.org/10.1145/2972958.2972967openalex-snapshot
Data Sets and Data Quality in Software Engineering
inferredcites →https://doi.org/10.1145/3035918.3064029openalex-snapshot
Automatic Database Management System Tuning Through Large-scale Machine Learning
inferredcites →https://doi.org/10.1145/3106237openalex-snapshot
Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
inferredcites →https://doi.org/10.1145/3106237.3106257openalex-snapshot
Revisiting unsupervised learning for defect prediction
inferredcites →https://doi.org/10.1145/3180155.3180197openalex-snapshot
Is "better data" better than "better data miners"?
inferredcites →https://doi.org/10.1145/3183519openalex-snapshot
Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice
inferredcites →https://doi.org/10.1146/annurev.psych.59.103006.093735openalex-snapshot
Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation
inferredcites →https://doi.org/10.1177/1745691614528520openalex-snapshot
Sailing From the Seas of Chaos Into the Corridor of Stability
inferredcites →https://doi.org/10.1201/b19467openalex-snapshot
Evidence-Based Software Engineering and Systematic Reviews
inferredcites →https://doi.org/10.1214/aos/1013699998openalex-snapshot
The control of the false discovery rate in multiple testing under dependency
inferredcites →https://doi.org/10.1214/ss/1009213004openalex-snapshot
Sensitivity Anaysis as an Ingredient of Modeling
inferredcites →https://doi.org/10.1371/journal.pbio.2000797openalex-snapshot
Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature
inferredcites →https://doi.org/10.1371/journal.pmed.0020124openalex-snapshot
Why Most Published Research Findings Are False
inferredcites →https://doi.org/10.1371/journal.pone.0162874openalex-snapshot
Prediction Interval: What to Expect When You’re Expecting … A Replication
inferredcites →https://doi.org/10.2307/1271419openalex-snapshot
Randomization, Bootstrap and Monte Carlo Methods in Biology
inferredcites →https://doi.org/10.2307/2533527openalex-snapshot
Randomization, Bootstrap and Monte Carlo Methods in Biology.
inferredcites →https://doi.org/10.2307/2981753openalex-snapshot
Understanding Robust and Exploratory Data Analysis.
inferredcites →https://doi.org/10.3233/jifs-169146openalex-snapshot
Would wider adoption of reproducible research be beneficial for empirical software engineering research?
inferredcites →https://doi.org/10.3389/fpsyg.2015.00621openalex-snapshot
Replication, falsification, and the crisis of confidence in social psychology
inferredcites →https://doi.org/10.4135/9781483398105openalex-snapshot
Methods of Meta-Analysis: Correcting Error and Bias in Research Findings
inferredcites →https://doi.org/10.48550/arxiv.1206.2944openalex-snapshot
Practical Bayesian Optimization of Machine Learning Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1407.5296openalex-snapshot
An investigation of the false discovery rate and the misinterpretation of P values
inferredcites →https://doi.org/10.48550/arxiv.1609.02613openalex-snapshot
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
inferredcites →https://doi.org/10.48550/arxiv.1710.08736openalex-snapshot
What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)
inferredcites →https://doi.org/10.5555/1756006.1859921openalex-snapshot
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
inferredcites →https://doi.org/10.5555/998675.999432openalex-snapshot
Evidence-based software engineering
inferredcites →https://doi.org/10.5860/choice.47-0914openalex-snapshot
Making sense of data II: a practical guide to data visualization, advanced data mining methods, and applications
inferredcites →https://doi.org/10.5860/choice.48-5742openalex-snapshot
The essential guide to effect sizes: statistical power, meta-analysis, and the interpretation of research results
inferredcites →https://doi.org/10.5860/choice.50-5018openalex-snapshot
Experimentation in software engineering
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/smr.2258openalex-snapshot
Data‐driven benchmarking in software development effort estimation: The few define the bulk
inferred← citeshttps://doi.org/10.1002/stvr.1787openalex-snapshot
An empirical study of Linespots: A novel past‐fault algorithm
inferred← citeshttps://doi.org/10.1007/978-3-030-32489-6_11openalex-snapshot
Bayesian Data Analysis in Empirical Software Engineering: The Case of Missing Data
inferred← citeshttps://doi.org/10.1007/s10664-020-09808-9openalex-snapshot
Better software analytics via “DUO”: Data mining algorithms using/used-by optimizers
inferred← citeshttps://doi.org/10.1007/s10664-020-09851-6openalex-snapshot
Publish or perish, but do not forget your software artifacts
inferred← citeshttps://doi.org/10.1007/s10664-020-09906-8openalex-snapshot
How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization)
inferred← citeshttps://doi.org/10.1007/s10664-021-10009-1openalex-snapshot
Conclusion stability for natural language based mining of design discussions
inferred← citeshttps://doi.org/10.1007/s10664-022-10243-1openalex-snapshot
Evaluating classifiers in SE research: the ECSER pipeline and two replication studies
inferred← citeshttps://doi.org/10.1016/j.infsof.2026.108197openalex-snapshot
How not to get your paper rejected — From the editors’ notebook
inferred← citeshttps://doi.org/10.1016/j.jss.2023.111684openalex-snapshot
Empirical research in software architecture — Perceptions of the community
inferred← citeshttps://doi.org/10.1016/j.jss.2024.111968openalex-snapshot
On the effectiveness of developer features in code smell prioritization: A replication study
inferred← citeshttps://doi.org/10.1109/asew.2019.00039openalex-snapshot
Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice
inferred← citeshttps://doi.org/10.1109/icsme.2019.00064openalex-snapshot
Processing Large Datasets of Fined Grained Source Code Changes
inferred← citeshttps://doi.org/10.1109/models.2019.00-19openalex-snapshot
Pitfalls Analyzer: Quality Control for Model-Driven Data Science Pipelines
inferred← citeshttps://doi.org/10.1109/ms.2021.3098670openalex-snapshot
Ground-Truth Deficiencies in Software Engineering: When Codifying the Past Can Be Counterproductive
inferred← citeshttps://doi.org/10.1109/msr52588.2021.00059openalex-snapshot
Predicting Design Impactful Changes in Modern Code Review: A Large-Scale Empirical Study
inferred← citeshttps://doi.org/10.1109/saner48275.2020.9054792openalex-snapshot
Cross-Dataset Design Discussion Mining
inferred← citeshttps://doi.org/10.1109/tse.2019.2935974openalex-snapshot
Bayesian Data Analysis in Empirical Software Engineering Research
inferred← citeshttps://doi.org/10.1109/tse.2020.3023177openalex-snapshot
Predicting Defective Lines Using a Model-Agnostic Technique
inferred← citeshttps://doi.org/10.1109/tse.2020.3048991openalex-snapshot
A Method to Assess and Argue for Practical Significance in Software Engineering
inferred← citeshttps://doi.org/10.1109/tse.2021.3113558openalex-snapshot
Use and Misuse of the Term “Experiment” in Mining Software Repositories Research
inferred← citeshttps://doi.org/10.1109/tse.2022.3174092openalex-snapshot
SEGRESS: Software Engineering Guidelines for REporting Secondary Studies
inferred← citeshttps://doi.org/10.1109/tse.2025.3526730openalex-snapshot
Evidence-Based Software Engineering Guidelines Revisited
inferred← citeshttps://doi.org/10.1145/3340495.3342752openalex-snapshot
Integrating runtime data with development data to monitor external quality: challenges from practice
inferred← citeshttps://doi.org/10.1145/3490953openalex-snapshot
Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
inferred← citeshttps://doi.org/10.1145/3611667openalex-snapshot
Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions
inferred← citeshttps://doi.org/10.1145/3674805.3690754openalex-snapshot
Crossover Designs in Software Engineering Experiments: Review of the State of Analysis
inferred← citeshttps://doi.org/10.1145/3799711openalex-snapshot
Exploring the Garden of Forking Paths in Empirical Software Engineering Research: A Multiverse Analysis
inferred← citeshttps://doi.org/10.17771/pucrio.acad.55274openalex-snapshot
UNVEILING SOCIAL AND TECHNICAL FACETS OF DESIGN DEGRADATION IN MODERN CODE REVIEW
inferred← citeshttps://doi.org/10.19053/01211129.v31.n60.2022.14189openalex-snapshot
Semi-Automatic Mapping Technique Using Snowballing to Support Massive Literature Searches in Software Engineering
inferred← citeshttps://doi.org/10.3390/info16090774openalex-snapshot
A Generalized Method for Filtering Noise in Open-Source Project Selection
inferred← citeshttps://doi.org/10.48550/arxiv.1905.06872openalex-snapshot
Better Security Bug Report Classification via Hyperparameter Optimization
inferred← citeshttps://doi.org/10.48550/arxiv.2001.01424openalex-snapshot
Cross-Dataset Design Discussion Mining
inferred← citeshttps://doi.org/10.5753/jserd.2026.7689openalex-snapshot
Revisiting the Energy Footprint of Small Language Models for Unit Test Generation: A Bayesian Comparative Study
inferred← citeshttps://doi.org/10.5753/sast.2025.14036openalex-snapshot
On the Energy Footprint of Using a Small Language Model for Unit Test Generation
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