Key papers in Software
Pofolia’s corpus holds 86 papers from the Software subfield (2012–2024). The list below starts with the most cited.
Most cited
Ranked by citation count. Because citations accumulate over time, this list naturally leans towards work published a few years ago; for where the field is now, see “recently added”.
Primer3—new capabilities and interfaces
Nucleic Acids Research · 2012 · Q1 · SJR 8.00 · FWCI 475.25 · 9,855 citations · Open access
The widely-used Primer3 software for designing DNA primers has been significantly enhanced with more accurate thermodynamic models for improved melting temperature prediction and reduced primer hairpin/dimer formation.
<scp>UCSF ChimeraX</scp>: Tools for structure building and analysis
Protein Science · 2023 · Q1 · SJR 3.00 · FWCI 780.74 · 4,277 citations · Open access
UCSF ChimeraX has introduced new computational tools that leverage machine learning for atomic model building of large molecular assemblies, improving accuracy at resolutions of 3-4 Å.
Software Metrics: A Rigorous and Practical Approach
2013 · FWCI 299.92 · 2,542 citations
This book offers a comprehensive and updated guide to software metrics, building on decades of progress in the field.
Defects4J: a database of existing faults to enable controlled testing studies for Java programs
2014 · 1,355 citations
Defects4J is a new database and framework providing 357 real bugs from 5 open-source Java programs to enable reproducible software testing research.
Regression testing minimization, selection and prioritization: a survey
Software Testing Verification and Reliability · 2012 · Q2 · FWCI 166.93 · 1,312 citations
This survey consolidates research on techniques to reduce the cost of regression testing by minimizing, selecting, or prioritizing test cases, addressing the challenge of ever-growing test suites in evolving software.
A comprehensive survey on safe reinforcement learning
Journal of Machine Learning Research · 2015 · Q1 · SJR 1.00 · FWCI 58.92 · 1,196 citations
This survey categorizes and analyzes two primary approaches to Safe Reinforcement Learning (SRL): modifying the optimality criterion with a safety factor, and altering the exploration process using external knowledge or risk metrics.
A Survey on Software Fault Localization
IEEE Transactions on Software Engineering · 2016 · Q1 · SJR 1.00 · FWCI 197.96 · 1,083 citations
This survey categorizes and reviews a wide range of software fault localization techniques designed to automatically pinpoint bugs in complex programs.
Introduction to Software Testing
Cambridge University Press eBooks · 2021 · FWCI 38.49 · 1,076 citations
This book presents a practical, step-by-step approach to software testing, integrating art, craft, and science through a single, consistent worked example.
The Oracle Problem in Software Testing: A Survey
IEEE Transactions on Software Engineering · 2014 · Q1 · SJR 1.00 · FWCI 57.64 · 1,043 citations · Open access
This survey analyzes current approaches and trends for automating the 'test oracle problem' in software testing, which is the challenge of distinguishing correct system behavior from incorrect behavior given an input.
Power to the People: The Role of Humans in Interactive Machine Learning
AI Magazine · 2014 · Q2 · FWCI 47.61 · 1,030 citations · Open access
Interactive machine learning systems are increasingly common, but progress is shifting from solely algorithmic advances to understanding and integrating the human user. This paper argues for a user-centric approach to improve both user experience and system effectiveness.
Unified Modeling Language User Guide, The (2nd Edition) (Addison-Wesley Object Technology Series)
2017 · FWCI 18.95 · 905 citations
This revised guide updates the industry-standard Unified Modeling Language (UML) to version 2.0, offering a tutorial on its core aspects with new content on components, internal structure, and updated coverage of diagrams and profiles.
Driller: Augmenting Fuzzing Through Selective Symbolic Execution
2016 · 885 citations · Open access
Driller is a new technique that combines fuzzing with selective symbolic execution to discover deeper software vulnerabilities.
Symbolic execution for software testing
Communications of the ACM · 2013 · Q1 · SJR 1.00 · FWCI 105.17 · 756 citations
Symbolic execution offers significant promise for modern software testing, despite inherent challenges in its implementation.
Directed Greybox Fuzzing
2017 · 729 citations
Directed Greybox Fuzzing (DGF) efficiently generates inputs to reach specific program locations, outperforming existing undirected fuzzing and directed symbolic execution.
Dynodroid: an input generation system for Android apps
2013 · 707 citations
Dynodroid is a new system that generates relevant inputs for unmodified Android apps by treating them as event-driven programs. It monitors app reactions to events to guide the generation of subsequent inputs.
Domain-specific languages
2012 · 660 citations
This book serves as a comprehensive guide to Domain Specific Languages (DSLs), detailing various techniques for their creation and application.
Challenges in Autonomous Vehicle Testing and Validation
SAE International Journal of Transportation Safety · 2016 · Q4 · FWCI 93.00 · 649 citations
Autonomous vehicle development requires a more methodical testing approach than traditional bug hunting to ensure safety at scale. This paper identifies five key challenges in applying the ISO 26262 V-model process to autonomous systems.
Coverage-based Greybox Fuzzing as Markov Chain
2016 · 623 citations
This paper models Coverage-based Greybox Fuzzing (CGF) as a Markov chain, revealing that prioritizing low-frequency paths significantly increases exploration efficiency.
Evaluating Fuzz Testing
2018 · 609 citations · Open access
A survey of 32 fuzz testing papers revealed significant flaws in the experimental evaluations of new fuzzing techniques, with problems found in every study considered.
Metamorphic Testing
ACM Computing Surveys · 2018 · Q1 · SJR 5.00 · FWCI 142.00 · 601 citations
Metamorphic testing is a software testing technique that uses 'metamorphic relations' to verify program correctness and generate test cases, even without known correct outputs.
Recently added
Software Testing With Large Language Models: Survey, Landscape, and Vision
IEEE Transactions on Software Engineering · 2024 · Q1 · SJR 1.00 · FWCI 196.18 · 419 citations
A survey of 102 studies reveals Large Language Models (LLMs) are increasingly applied to software testing, particularly for test case preparation and program repair.
<scp>UCSF ChimeraX</scp>: Tools for structure building and analysis
Protein Science · 2023 · Q1 · SJR 3.00 · FWCI 780.74 · 4,277 citations · Open access
UCSF ChimeraX has introduced new computational tools that leverage machine learning for atomic model building of large molecular assemblies, improving accuracy at resolutions of 3-4 Å.
Code Llama: Open Foundation Models for Code
arXiv (Cornell University) · 2023 · 411 citations · Open access
Meta AI releases Code Llama, a family of open-source large language models for code generation and understanding, achieving state-of-the-art performance among open models on benchmarks like HumanEval (67%) and MBPP (65%).
Automated Program Repair in the Era of Large Pre-trained Language Models
2023 · 343 citations
Directly applying state-of-the-art Large Language Models (LLMs) for Automated Program Repair (APR) substantially outperforms existing techniques, fixing more bugs across multiple languages and datasets.
Use Chat GPT to Solve Programming Bugs
International Journal of Information technology and Computer Engineering · 2023 · FWCI 68.04 · 381 citations · Open access
Chat GPT can assist in debugging by providing explanations, predictions, and direct solutions for programming bugs.
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