Software Technologies : 18th International Conference, ICSOFT 2023, Rome, Italy, July 10-12, 2023, Revised Selected Papers

• Record Nr.: UNINA9910864199303321
• Autore: Fill Hans-Georg
• Pubbl/distr/stampa: Cham : , : Springer, , 2024; ©2024
• ISBN: 3-031-61753-3
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (166 pages)
• Collana: Communications in Computer and Information Science Series ; ; v.2104
• Altri autori (Persone): Domínguez MayoFrancisco José; van SinderenMarten; MaciaszekLeszek A
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Green Evolutionary Algorithms and JavaScript: A Study on Different Software and Hardware Architectures -- 1 Introduction -- 2 State of the Art -- 3 Methodology and Experimental Setup -- 3.1 Measurements on an Intel-Based Platform -- 3.2 Measurements on the Apple Silicon Platform -- 3.3 Running the Experiments and Processing the Results -- 4 Experimental Results -- 4.1 Comparing Performance of JS Interpreters -- 4.2 Comparison Between Different Interpreter Versions -- 5 Conclusions -- References -- Low-Modeling of Software Systems -- 1 Introduction -- 2 Low Modeling Definition -- 3 Low Modeling Strategies -- 3.1 Heuristic-Based Model Generation -- 3.2 Knowledge-Based Model Enrichment -- 3.3 ML-Based Model Inference -- 4 Case Study: Low Modeling of Conversational Interfaces -- 4.1 From Tabular Data to Data Models -- 4.2 From Data Models to Conversational Models -- 4.3 From Conversational Models to the Actual Chatbot -- 5 BESSER: A Low-Code Low-Modeling Platform -- 6 Conclusions and Further Work -- References -- Model-Driven Security Smell Resolution in Microservice Architecture Using LEMMA -- 1 Introduction -- 2 Preliminaries -- 2.1 Viewpoint-Based Microservice Modeling with LEMMA -- 2.2 Smells and Refactorings for Microservice Security -- 2.3 Software Architecture Reconstruction -- 3 Case Study Introduction -- 4 Model-Driven ; Security Smell Resolution -- 4.1 Model-Based Reconstruction of Microservice Architecture -- 4.2 Security Smell Modeling for Microservice Architecture -- 4.3 Security Smells Detection for Microservice Architecture -- 4.4 Security Smell Resolving for Microservice Architecture -- 5 Case Study Validation -- 6 Discussion -- 7 Related Work -- 8 Conclusions -- References.; Indentation and Reading Time: A Controlled Experiment on the Differences Between Generated Indented and Non-indented JSON Objects -- 1 Introduction -- 2 Indentation and Its Possible Effects -- 2.1 Indentation: A Brief Description -- 2.2 Possible Effects of Indentation on Reading -- 3 Related Work -- 4 Experiment -- 4.1 Initial Considerations -- 4.2 Experiment Layout -- 4.3 Protocol -- 4.4 Execution -- 4.5 Possible Outliers -- 4.6 Analysis -- 4.7 Analysis per Participant -- 4.8 Discussion So Far -- 5 Exploratory Analyses: The Authors' Results -- 6 Threats to Validity -- 7 Summary and Discussion -- 8 Conclusion -- References -- The Sustainable User Experiences Enabled Human-Centered Framework for Systems Design -- 1 Introduction -- 2 Related Work -- 3 Overview of the SUXEH Framework and Its Components -- 3.1 The Enabling Goals Component -- 3.2 The User Experience Outcomes Component -- 3.3 The Functionality Goals Component -- 4 The Framework Guidelines Towards Requirements Specifications -- 4.1 Identification of Human Needs -- 4.2 Defining Human Needs (Enabling Goals) Within Technology -- 4.3 Refining Enabling Goals into Sub-Goals -- 4.4 Deriving Functionality Goals from Sub-Goals -- 4.5 Analysing Functionality Goals for Their Impact on Other System Goals -- 5 The Taxi System: A Demonstration Case Study -- 5.1 Deriving the Taxi System Functionality Goals from Sub-Goals -- 5.2 Analysing the Taxi System Functionality Goals for Conflicts -- 6 Discussion -- 7 Conclusion and Future Work -- References -- DeepPull: Deep Learning-Based Approach for Predicting Reopening, Decision, and Lifetime of Pull Requests on GitHub Open-Source Projects -- 1 Introduction -- 2 Background -- 2.1 GitHub Pull-Based Development Model -- 2.2 Current Challenges -- 3 Related Work -- 3.1 Pull Request Reopening -- 3.2 Pull Request Decision -- 3.3 Pull Request Lifetime.; 3.4 Gaps in Literature and Motivations -- 4 Predictive Problem Formulation -- 5 Research Design -- 5.1 Research Questions -- 5.2 Experimental Procedure and Metrics -- 6 DeepPull -- 6.1 Overview Framework -- 6.2 Feature Extraction -- 6.3 Oversampling Approach -- 6.4 Model Training -- 7 Evaluation -- 7.1 Data Collection and Sampling -- 7.2 Data Splitting -- 7.3 Experimental Results -- 7.4 Further Findings -- 7.5 Threats to Validity -- 8 Conclusions and Future Work -- References -- On the Relevance of Graph2Vec Source Code Embeddings for Software Defect Prediction -- 1 Introduction -- 2 Related Work -- 3 The Relevance of the Graph2Vec Embedding for SDP -- 3.1 Proposed Representations -- 3.2 The Considered ML Models -- 3.3 Case Study -- 3.4 Evaluation Methodology -- 3.5 Results and Analysis -- 4 FastAI Versus Tuned Random Forest as Defect Prediction Models -- 4.1 Hyperparameter Tuning -- 4.2 Comparison of FastAI and Random Forest -- 4.3 Analysis of the Hyperparameter Tuning Process -- 4.4 Analysis of the Best Hyperparameters -- 5 Adding a Local Search to Hyperparameter Tuning -- 5.1 The Local Search -- 5.2 Results and Analysis -- 6 The Relevance of SYNMET Metrics for SDP -- 6.1 The SYNMET Metrics Suite -- 6.2 SYNMET's Relevance for SDP Analyses -- 7 Conclusions -- References -- Author Index.