The Effects Of Customer Dissatisfaction On Switching Behavior In The Service Sector

Customer acceptance in the online environment has been drastically changed due to the presence of the Internet. After adopting products, customers’ willingness to adopt services in the online environment has received increased attention. This study explores how customers are willing to switch from offline to online services by examining i) the factors of dissatisfaction in the offline service environment; ii) how overall dissatisfaction affects regret and complaining behavior; and iii) how the level of regret and complaining behavior affects switching behavior. Proposed relationships are developed based on the theoretical background of satisfaction/dissatisfaction in the virtualized environment. By applying various statistical analyses, this study identifies managerial and theoretical implications and offers suggestions for the management of e-business customer relationships

Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing

GUI testing checks if a software system behaves as expected when users interact with its graphical interface, e.g., testing specific functionality or validating relevant use case scenarios. Currently, deciding what to test at this high level is a manual task since automated GUI testing tools target lower level adequacy metrics such as structural code coverage or activity coverage. We propose DroidAgent, an autonomous GUI testing agent for Android, for semantic, intent-driven automation of GUI testing. It is based on Large Language Models and support mechanisms such as long- and short-term memory. Given an Android app, DroidAgent sets relevant task goals and subsequently tries to achieve them by interacting with the app. Our empirical evaluation of DroidAgent using 15 apps from the Themis benchmark shows that it can set up and perform realistic tasks, with a higher level of autonomy. For example, when testing a messaging app, DroidAgent created a second account and added a first account as a friend, testing a realistic use case, without human intervention. On average, DroidAgent achieved 61% activity coverage, compared to 51% for current state-of-the-art GUI testing techniques. Further, manual analysis shows that 317 out of the 374 autonomously created tasks are realistic and relevant to app functionalities, and also that DroidAgent interacts deeply with the apps and covers more features.Comment: 10 page

Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction

Many automated test generation techniques have been developed to aid developers with writing tests. To facilitate full automation, most existing techniques aim to either increase coverage, or generate exploratory inputs. However, existing test generation techniques largely fall short of achieving more semantic objectives, such as generating tests to reproduce a given bug report. Reproducing bugs is nonetheless important, as our empirical study shows that the number of tests added in open source repositories due to issues was about 28% of the corresponding project test suite size. Meanwhile, due to the difficulties of transforming the expected program semantics in bug reports into test oracles, existing failure reproduction techniques tend to deal exclusively with program crashes, a small subset of all bug reports. To automate test generation from general bug reports, we propose LIBRO, a framework that uses Large Language Models (LLMs), which have been shown to be capable of performing code-related tasks. Since LLMs themselves cannot execute the target buggy code, we focus on post-processing steps that help us discern when LLMs are effective, and rank the produced tests according to their validity. Our evaluation of LIBRO shows that, on the widely studied Defects4J benchmark, LIBRO can generate failure reproducing test cases for 33% of all studied cases (251 out of 750), while suggesting a bug reproducing test in first place for 149 bugs. To mitigate data contamination, we also evaluate LIBRO against 31 bug reports submitted after the collection of the LLM training data terminated: LIBRO produces bug reproducing tests for 32% of the studied bug reports. Overall, our results show LIBRO has the potential to significantly enhance developer efficiency by automatically generating tests from bug reports.Comment: Accepted to IEEE/ACM International Conference on Software Engineering 2023 (ICSE 2023

Towards Autonomous Testing Agents via Conversational Large Language Models

Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. The recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized hallucination of LLMs can be beneficial while testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss some potential limitations

The GitHub Recent Bugs Dataset for Evaluating LLM-based Debugging Applications

Large Language Models (LLMs) have demonstrated strong natural language processing and code synthesis capabilities, which has led to their rapid adoption in software engineering applications. However, details about LLM training data are often not made public, which has caused concern as to whether existing bug benchmarks are included. In lieu of the training data for the popular GPT models, we examine the training data of the open-source LLM StarCoder, and find it likely that data from the widely used Defects4J benchmark was included, raising the possibility of its inclusion in GPT training data as well. This makes it difficult to tell how well LLM-based results on Defects4J would generalize, as for any results it would be unclear whether a technique's performance is due to LLM generalization or memorization. To remedy this issue and facilitate continued research on LLM-based SE, we present the GitHub Recent Bugs (GHRB) dataset, which includes 76 real-world Java bugs that were gathered after the OpenAI data cut-off point

A comparison of food and nutrient intake between instant noodle consumers and non-instant noodle consumers in Korean adults

Instant noodles are widely consumed in Asian countries. The Korean population consumed the largest quantity of instant noodles in the world in 2008. However, few studies have investigated the relationship between instant noodles and nutritional status in Koreans. The objective of this study was to examine the association between instant noodle consumption and food and nutrient intake in Korean adults. We used dietary data of 6,440 subjects aged 20 years and older who participated in the Korean National Health and Nutrition Examination Survey III. The average age of the instant noodle consumers (INC) was 36.2 and that of the non-instant noodle consumers (non-INC) was 44.9; men consumed more instant noodles than women (P < 0.001). With the exception of cereals and grain products, legumes, seaweeds, eggs, and milk and dairy products, INC consumed significantly fewer potatoes and starches, sugars, seeds and nuts, vegetables, mushrooms, fruits, seasonings, beverages, meats, fishes, and oils and fats compared with those in the non-INC group. The INC group showed significantly higher nutrient intake of energy, fat, sodium, thiamine, and riboflavin; however, the INC group showed a significantly lower intake of protein, calcium, phosphorus, iron, potassium, vitamin A, niacin, and vitamin C compared with those in the non-INC group. This study revealed that consuming instant noodles may lead to excessive intake of energy, fats, and sodium but may also cause increased intake of thiamine and riboflavin. Therefore, nutritional education helping adults to choose a balanced meal while consuming instant noodles should be implemented. Additionally, instant noodle manufacturers should consider nutritional aspects when developing new products

Stretchable Supercapacitors Based on Carbon Nanotubes-Deposited Rubber Polymer Nanofibers Electrodes with High Tolerance against Strain

We report a new fabrication method for a fully stretchable supercapacitor based on single wall carbon nanotube (SWCNT)-coated electrospun rubber nanofibers as stretchable supercapacitor electrodes. The deposition conditions of SWCNT on hydrophobic rubber nanofibers are experimentally optimized to induce a uniform coating of SWCNT. For surfactant-assisted coating of SWCNT, both water contact angle and sheet resistance were lower compared to the cases with other surface treatment methods, indicating a more effective coating approach. The excellent electromechanical properties of this electrode under stretching conditions are demonstrated by the measurement of Young&rsquo;s modulus and normalized sheet resistance. The superb tolerance of the electrode with respect to stretching is the result of (i) high aspect ratios of both nanofiber templates and the SWCNT conductors, (ii) the highly elastic nature of rubbery nanofibers, and (iii) the strong adherence of SWCNT-coated nanofibers on the elastic ecoflex substrate. Electrochemical and electromechanical measurements on stretchable supercapacitor devices reveal that the volumetric capacitance (15.2 F cm&minus;3 at 0.021 A cm&minus;3) of the unstretched state is maintained for strains of up to 40%. At this level of strain, the capacitance after 1,000 charge/discharge cycles was not significantly reduced. The high stability of our stretchable device suggests potential future applications in various types of wearable energy storage devices

Higher Quantum State Transitions in Colloidal Quantum Dot with Heavy Electron Doping

Electron occupation in the lowest quantized state of the conduction band (1S_e) in the colloidal quantum dot leads to the intraband transition in steady-state (1S_e-1P_e). The intraband transition, solely originating from the quantum confinement effect, is the unique property of semiconducting nanocrystals. To achieve the electron occupation in 1S_e state in the absence of impurity ions, nonthiol ligand passivated HgS colloidal quantum dots are synthesized. The nonthiol ligand passivated HgS quantum dot exhibits strong steady-state intraband transition in ambient condition and enables a versatile ligand replacement to oxide, acid, and halide functional ligands, which was not achievable from conventional HgS or HgSe quantum dots. Surprisingly, the atomic ligand passivation to HgS colloidal quantum dot solution efficiently maintains the electron occupation at 1S_e of HgS CQDs in ambient condition. The electron occupation in 1S_e of HgS CQD solid film is controlled by surface treatment with charged ions, which is confirmed by the mid-IR intraband absorption (1S_e-1P_e) intensity imaged by the FTIR microscope. Furthermore, a novel second intraband transition (1P_e-1D_e) is observed from the HgS CQD solid. The observation of the second intraband transition (1P_e-D_e) allows us to utilize the higher quantized states that were hidden for the last three decades. The use of the intraband transition with narrow bandwidth in mid-IR would enable to choose an optimal electronic transition occurring in the nanocrystal for a number of applications: wavelength-selective low-energy consuming electronics, space-communication light source, mid-infrared energy sensitized electrode and catalyst, infrared photodetector, and infrared filter