Characterizing Natural Fractures and Their Interactions with Hydraulically Induced Fractures

Natural fractures are preexisting micro-cracks and fissures that can have a critical impact on hydraulic fracture treatments in shales. Most shale formations contain natural fractures, but the characteristics of these natural fractures can vary significantly. For example, the natural fractures in the Barnett Shale are mostly narrow, long, and sealed with calcite cement. The natural fractures in the Wolfcamp Shale are much more heterogeneous as a whole, but tend to be clustered in similar groupings based on the lithology of certain areas of the formation. The creation and development of natural fractures prior to any hydraulic fracturing treatments is primarily a function of mineralogy, total organic carbon, and in-situ stresses. During hydraulic fracturing treatments, certain characteristics, such as the relative angle between the natural and hydraulic fractures, the length of the natural fractures, the differential stress of the formation rock, and certain completion design variables, will determine how the natural and induced fractures interact and create a fracture network. The creation of a natural fracture network can have a positive effect on the ultimate hydrocarbon recovery in some cases. Natural fractures provide accumulation space and travel pathways for hydrocarbons, which is critical in low porosity and low permeability shales. However, natural fractures can result in higher rates of fluid leakoff, which will result in less efficient hydraulic fracture treatments overall. Also, natural fractures can provide an undesirable connection to water accumulations, which can negatively impact the economics of a well because of the disposal costs associated with water production. This thesis seeks to characterize natural fractures and also to describe the author's work on a hydraulic fracture simulation software that takes the impact of natural fractures into account.Petroleum and Geosystems Engineerin

What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure

The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study

Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 102 publications. Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.Comment: Under submission to Software Testing, Verification, and Reliability journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an earlier study that this study extends

Enhancing Web Applications Observability through Instrumented Automated Browsers

In software engineering, observability is the ability to determine the current state of a software system based on its external outputs or signals such as metrics, logs, or traces. Web engineers rely on the web browser console as the primary tool to monitor the client-side of web applications during end-to-end tests. However, this is a manual and time-consuming task due to the different browsers available. This paper presents BrowserWatcher, an open-source browser extension providing cross-browser capabilities to observe web applications and automatically gather browser console logs in different browsers (e.g., Chrome, Firefox, or Edge). We have leveraged this extension to conduct an empirical study analyzing the browser console of the top-50 public websites manually and automatically. The results show that BrowserWatcher gathers all the well-known log categories such as console or error traces. It also reveals that each web browser additionally includes other types of logs, which differ among browsers, thus providing distinct pieces of information for the same website.This work was partially supported in part by the Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación, Spain (10.13039/501100011033) through the H2O Learn project under Grant PID2020-112584RB-C31, in part by the Madrid Regional Government through the e-Madrid-CM Project, Spain under Grant S2018/TCS-4307, and in part supported by the Comunidad de Madrid and Universidad Politécnica de Madrid, Spain through the V-PRICIT Research Programme Apoyo a la realización de Proyectos de I+D para jóvenes investigadores UPM-CAM, under Grant APOYOJOVENES-QINIM8-72-PKGQ0J. Funding for Article Processing Charge (APC): Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)

Large Language Models in Fault Localisation

Large Language Models (LLMs) have shown promise in multiple software engineering tasks including code generation, code summarisation, test generation and code repair. Fault localisation is essential for facilitating automatic program debugging and repair, and is demonstrated as a highlight at ChatGPT-4's launch event. Nevertheless, there has been little work understanding LLMs' capabilities for fault localisation in large-scale open-source programs. To fill this gap, this paper presents an in-depth investigation into the capability of ChatGPT-3.5 and ChatGPT-4, the two state-of-the-art LLMs, on fault localisation. Using the widely-adopted Defects4J dataset, we compare the two LLMs with the existing fault localisation techniques. We also investigate the stability and explanation of LLMs in fault localisation, as well as how prompt engineering and the length of code context affect the fault localisation effectiveness. Our findings demonstrate that within a limited code context, ChatGPT-4 outperforms all the existing fault localisation methods. Additional error logs can further improve ChatGPT models' localisation accuracy and stability, with an average 46.9% higher accuracy over the state-of-the-art baseline SmartFL in terms of TOP-1 metric. However, performance declines dramatically when the code context expands to the class-level, with ChatGPT models' effectiveness becoming inferior to the existing methods overall. Additionally, we observe that ChatGPT's explainability is unsatisfactory, with an accuracy rate of only approximately 30%. These observations demonstrate that while ChatGPT can achieve effective fault localisation performance under certain conditions, evident limitations exist. Further research is imperative to fully harness the potential of LLMs like ChatGPT for practical fault localisation applications

An Evaluation of Log Parsing with ChatGPT

Software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks. However, its performance in automated log parsing remains unclear. In this paper, we evaluate ChatGPT's ability to undertake log parsing by addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) How does ChatGPT perform with different prompting methods? Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting. Based on our findings, we outline several challenges and opportunities for ChatGPT-based log parsing.Comment: 6 page