Firefly algorithm improves software effort estimation models.
problem Improving accuracy of software effort estimation models.
method Using Firefly Algorithm to optimize COCOMO-based models.
result High accuracy and significant error minimization of Firefly Algorithm.
Research proposes an ensemble learning model for efficient software defect prediction.
problem Efficient and cost-effective software testing to minimize project resources.
method Machine learning analysis on different datasets using KNN, Decision Tree, SVM, and Naïve Bayes.
result Ensemble learning model outperforms other techniques in accuracy, precision, recall, and F1-score.
Deep model improves software code understanding.
problem Software code's long context challenges existing models.
method Inspired by human memory, uses LSTM for long-term dependencies.
result Demonstrated effectiveness on Java project corpus.
Python package for projecting onto quadratic hypersurfaces.
problem Projections onto non-cylindrical central quadratic hypersurfaces.
method User-friendly Python package with documentation.
result Efficiently projects points onto quadratic hypersurfaces.
Import2vec creates embeddings for software libraries to improve learning tasks.
problem Developing semantic representations for software libraries.
method Applied word embedding techniques from NLP to library packages.
result Library vectors capture meaningful relationships among libraries.
Improving software quality through effective organizational learning.
problem Lack of reliable quantification methods for software evolution.
method Leveraging application lifecycle management data to identify and address managerial practices.
result Effective learning from past processes improves software quality indirectly.
Deep learning model predicts story points in agile projects.
problem Estimating effort for user stories in agile projects.
method Combines LSTM and Recurrent Highway Network for end-to-end story point prediction.
result Model outperforms existing baselines and alternatives in effort estimation.
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
problem Insufficient performance of existing temporal models on extrapolated queries and time prediction.
method Introduced an extension to current temporal models using relative temporal information.
result Improved performance on extrapolated queries and time prediction.
This paper uses machine learning to assist automation engineers in decision making.
problem Imperfect decision making by automation engineers leads to multiple iterations and increased time for software development.
method Defined challenges and proposed solutions using machine learning for automation engineering, including code classification, finding similar code snippets, and hardware selection.
result Paragraph embedding techniques achieved an F1-score of 72% for classifying automation using code snippets, and autoencoder models for hardware recommendation achieved p@3 and p@5 of 0.79 and 0.95, respectively.
CK simplifies ML model deployment and reproducibility with open APIs and DevOps.
problem Making ML models reproducible and deployable across different environments.
method Decompose complex systems into reusable sub-components with unified APIs and DevOps principles.
result Automatically co-design and optimize ML models for speed, accuracy, energy, and size.
Graph-based ML improves defect prediction in software development.
problem Challenges in predicting defect-prone changes in complex software development.
method Building contribution graphs from developers and source files, using graph-based ML for defect prediction.
result Graph-based ML leads to significantly better defect prediction (F1 score up to 77.55%, MCC up to 53.16%).
The paper proposes using non-isotropic distances for more accurate trace link recommendation.
problem Time-consuming and error-prone creation and maintenance of trace links.
method Geometric viewpoint on semantic similarity using non-linear similarity measures.
result Non-isotropic distances improve trace link recommendation accuracy.
Replication study shows Deep-SE still not as effective as previously thought for agile effort estimation.
problem Improving accuracy in estimating agile software development effort.
method Close replication of Deep-SE using additional data and comparison with multiple baselines.
result Deep-SE outperforms only a few cases, suggesting more work is needed.
OS-ELM outperforms other classifiers in software defect prediction.
problem Predicting software defects from imbalanced datasets.
method OS-ELM, oversampling with noise filtering, comparison with other classifiers.
result OS-ELM outperforms other classifiers in recall and balanced accuracy.
knotR package creates visually appealing knot diagrams in R
problem Creating aesthetically pleasing knot diagrams
method Develops knotR package for R programming to optimize knot diagrams
result Systematic creation of production-quality knot artwork
CLN improves collective classification accuracy using deep learning.
problem Collective classification in multi-relational domains is computationally challenging.
method Column Network (CLN) model for collective classification in multi-relational domains.
result CLN achieves higher accuracy than state-of-the-art rivals in various applications.
Simple formulas for sphere eversions using ruled surfaces.
problem Complex equations for sphere eversions.
method Explicit formulas for sphere and Boy surfaces using ruled surfaces.
result Visualizable formulas for sphere eversions.
The paper reviews statistical models for high-dimensional normalized vectors.
problem Handling directional data in machine learning.
method Review of mathematical models for normalized vectors on hypersphere and real projective plane.
result Common models and technical aspects are discussed.
We examine some differential geometric approaches to finding approximate solutions to the continuous time nonlinear filtering problem. Our primary focus is a new projection method for the optimal filter infinite dimensional Stochastic Partial Differential Equation (SPDE), based on the direct L2 metric and on a family o…
Implementing large-scale information and communication technology (IT) projects carries large risks and easily might disrupt operations, waste taxpayers' money, and create negative publicity. Because of the high risks it is important that government leaders manage the attendant risks. We analysed a sample of 1,355 publ…
DRIFT uses RL to automate functional software testing efficiently.
problem Efficient and reliable automated software testing.
method DRIFT employs Q-learning with Graph Neural Networks on symbolic UI representations.
result DRIFT can robustly test software functionalities in a fully automated manner.
A new method prioritizes project risks using Monte Carlo Simulation.
problem Determining the relative importance of project risks.
method Monte Carlo Simulation (MCS) for quantitative prioritization.
result Differentiates critical risks based on their impact on project duration and cost.
Multimodal deep learning improves flaw detection in software programs.
problem Current flaw detection relies on single software representations.
method Adapted multimodal deep learning models for flaw detection.
result Multimodal models outperform traditional deep learning models.
KnotPlot helps beginners and veterans use software for visualizing knots.
problem None explicitly stated in the abstract.
method Software introduction and background for users.
result Provides new and veteran users with KnotPlot functionality.
This paper evaluates methods to categorize similar software packages in npm's network.
problem Finding similar packages in npm's large software dependency network.
method Using graph structure to categorize packages, focusing on npm's 216,911 packages.
result Developed methods for categorizing similar packages, useful for recommendation systems.
Out-of-control information technology (IT) projects have ended the careers of top managers, such as EADS CEO Noel Forgeard and Levi Strauss' CIO David Bergen. Moreover, IT projects have brought down whole companies, like Kmart in the US and Auto Windscreen in the UK. Software and other IT is now such an integral part o…
DeepSoft aims to model software development for risk prediction and intervention.
problem Manual feature engineering and traditional classification problems in software analytics.
method End-to-end deep learning framework using Long Short Term Memory architecture.
result DeepSoft predicts future risks and recommends interventions in software development.
Survey and benchmark high-dimensional Bayesian optimization of discrete sequences.
problem Heterogeneous experimental set-ups and technical barriers in high-dimensional Bayesian optimization of discrete sequences.
method Unified framework and software libraries to test and benchmark methods.
result Unified framework and software libraries for testing and benchmarking high-dimensional Bayesian optimization methods.
Improved software flaw detection using NAS on multimodal DL models.
problem Software flaw detection in multimodal deep learning models.
method Adapted NAS framework for multimodal learning, combined with multimodal deep learning models.
result Improved performance on the Juliet Test Suite.
System simplifies feedback from domain experts for iterative rule mining.
problem Difficulty in incorporating domain experts' feedback and evaluating multi-objective models.
method Developed a multi-objective anytime rule mining algorithm based on GRASP-PR.
result Successfully applied the system in an industrial context.
This paper advances theory on the process of collaboration between entities and its implications on the quality of services, information, and/or products (SIPs) that the collaborating entities provide to each other. It investigates the scenario of outsourced IS projects (such as custom software development) where the e…
Generative Adversarial Network repairs software bugs without labeled data.
problem Automated repair of software vulnerabilities.
method Adversarial learning approach mapping between source and target domains.
result Effective at repairing software vulnerabilities, close to seq2seq approaches.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
problem Predicting time-varying hardware resource availability in control software.
method Path structured multimarginal Schrödinger bridge (MSBP) for learning stochastic resource usage.
result Guaranteed linear convergence to accurate prediction of hardware resource utilization.
This paper tackles co-design of neural hardware and software to improve efficiency.
problem Designing efficient deep learning systems that consider both hardware and software optimizations together.
method Developed a constrained Bayesian optimization framework to automatically identify profitable design points in the joint hardware/software design space.
result Improved energy-delay product by 18% (ResNet) and 40% (DQN) over hand-tuned systems.
Machine learning enhances fuzzing for better software testing.
problem Challenges in traditional fuzzing.
method Applications of machine learning to improve fuzzing.
result Machine learning tools have successfully addressed fuzzing bottlenecks.
This study integrates spatial coordinates into pavement maintenance prioritization.
problem Limited consideration of spatial characteristics in pavement maintenance prioritization.
method Introduced a novel spatial clustering algorithm to combine projects within budget and spatial constraints.
result Minimizes crew routing and improves collaboration between maintenance teams.
Survey of software developers' experience with Github Copilot tool.
problem Investigate developers' acceptance of AI-generated code.
method Survey with 18 questions distributed to 42 programmers.
result Mixed developer opinions, mostly positive but reluctance to use.
Optuna introduces a new hyperparameter optimization framework.
problem Efficient hyperparameter optimization for machine learning models.
method Define-by-run API, efficient implementation, versatile architecture.
result Optuna is the first software meeting next-generation hyperparameter optimization criteria.
Adapts ISO 26262 for machine learning in automotive software.
problem Conflict between innovation and safety in automotive software development.
method Detailed assessment and adaptation of ISO 26262 for machine learning, analyzing and proposing new requirements.
result Adapted ISO 26262 for machine learning, addressing safety and innovation in automotive software.
RAMANMETRIX simplifies Raman spectroscopy data analysis.
problem Complexity of Raman spectroscopy data processing.
method User-friendly software with chemometric analysis workflow.
result Facilitates integration of Raman spectroscopy into clinical routines.
Software helps teach latent variable methods in multivariate data analytics.
problem Challenges in understanding multivariate data analytics for students.
method Interactive software for comparing latent variable methods.
result Builds intuition on choosing methods and interpreting coefficients.
SySeVR uses deep learning to detect software vulnerabilities.
problem Detecting software vulnerabilities is challenging and important.
method SySeVR combines syntax and semantic information to represent programs for deep learning.
result SySeVR detects 15 unknown vulnerabilities, including 7 unknown and 8 silently patched ones.
This study compares traditional ML and deep learning for classifying multilingual user feedback.
problem Classifying multilingual user feedback for software development teams.
method Comparison of traditional machine learning and deep learning approaches.
result Traditional machine learning can achieve comparable results to deep learning for multilingual user feedback classification.
EvaSylv software evaluates forest management with natural risk considerations.
problem Evaluating forest management under increased natural risk due to climate change.
method User-friendly software simulates forest management scenarios, integrating natural risk using a Poisson process and Faustmann approach.
result Software optimizes forest management criteria like Faustmann value and Averaged yield value.
This paper discusses challenges in integrating ML in software companies.
problem Challenges in integrating Machine Learning in software companies.
method Discussion of challenges faced by Atlassian.
result The importance of human involvement in ML systems.
PROPEDEUTICA detects malware efficiently in real-time with low overhead.
problem Real-time malware detection on safety-critical devices with performance constraints.
method PROPEDEUTICA uses a hybrid approach combining conventional ML and DL, with a novel DL architecture DEEPMALWARE.
result PROPEDEUTICA achieves high accuracy (94.34%) and low false-positive rate (8.75%) with minimal performance impact.
Software estimates inequality in random systems with changing communities.
problem Measuring inequality in systems with dynamic interactions and random attributes.
method Piecewise homogeneous Markov chain for changing points, copula function for multivariate distribution, Monte Carlo algorithm for entropy estimation.
result Estimates Random Theil's Entropy to measure inequality in random systems.
Devign uses graph neural networks to identify vulnerabilities efficiently.
problem Challenging and tedious process of identifying vulnerabilities in software systems.
method Devign employs a graph neural network to classify graph-level vulnerabilities using comprehensive code semantic representations.
result Devign significantly outperforms state-of-the-art models in vulnerability identification.