Adaptive REST API Testing with Reinforcement Learning

Modern web services increasingly rely on REST APIs. Effectively testing these APIs is challenging due to the vast search space to be explored, which involves selecting API operations for sequence creation, choosing parameters for each operation from a potentially large set of parameters, and sampling values from the virtually infinite parameter input space. Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally (i.e., not considering their importance or complexity) and lacking prioritization strategies. Furthermore, these tools struggle when response schemas are absent in the specification or exhibit variants. To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize operations and parameters during exploration. Our approach dynamically analyzes request and response data to inform dependent parameters and adopts a sampling-based strategy for efficient processing of dynamic API feedback. We evaluated our technique on ten RESTful services, comparing it against state-of-the-art REST testing tools with respect to code coverage achieved, requests generated, operations covered, and service failures triggered. Additionally, we performed an ablation study on prioritization, dynamic feedback analysis, and sampling to assess their individual effects. Our findings demonstrate that our approach outperforms existing REST API testing tools in terms of effectiveness, efficiency, and fault-finding ability.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Automated Test Generation for REST APIs: No Time to Rest Yet

Modern web services routinely provide REST APIs for clients to access their functionality. These APIs present unique challenges and opportunities for automated testing, driving the recent development of many techniques and tools that generate test cases for API endpoints using various strategies. Understanding how these techniques compare to one another is difficult, as they have been evaluated on different benchmarks and using different metrics. To fill this gap, we performed an empirical study aimed to understand the landscape in automated testing of REST APIs and guide future research in this area. We first identified, through a systematic selection process, a set of 10 state-of-the-art REST API testing tools that included tools developed by both researchers and practitioners. We then applied these tools to a benchmark of 20 real-world open-source RESTful services and analyzed their performance in terms of code coverage achieved and unique failures triggered. This analysis allowed us to identify strengths, weaknesses, and limitations of the tools considered and of their underlying strategies, as well as implications of our findings for future research in this area.Comment: 13 pages, 6 figures, In Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 202

Relationship of Resistance to Benzimidazole Fungicides with Mutation of β-Tubulin Gene in Venturia nashicola

Pear scab caused by Venturia nashicola has been reported as an important disease of pear resulting in lowering the quality of pear fruits. In this study, it was conducted to investigate the relationship between resistance of V. nashicola and mutation of β-tubulin gene and the fungicide resistance in field isolate group in benzimidazole fungicides. Responce of V. nashicola to carbendazim could be classified into 3 groups as sensitive that does not grow at all on PDA amended with 0.16 μg/ml of carbendazim, low resistance that could not grow in 4.0 μg/ml medium, and high resistance that can grow even at 100 μg/ml. Thirty isolates of V. nashicola collected from 3 regions as Wonju, Naju, and Okcheon were highly resistant to carbendazim. Analysis of the nucleotide sequence of β-tubulin gene of V. nashicola showed that there was no difference in the nucleotide sequence between the sensitive and the low-resistant isolate, but GAG at codon 198 (glutamic acid) was replaced with GCG (alanine) in the high-resistant isolate. Among 10 isolates obtained from the Okcheon, 5 isolates showed the substitution of glycine for glutamic acid, which were resistant to carbendazim, but more sensitive to the mixture of carbendazim and diethofencarb than others. Through these results, all isolates of V. nashicola isolated in pear orchard were found to be resistant to benzimidazoles. Also, mutants E198A and E198G at β-tubulin were found to be important mechanisms of V. nashicola resistance against benzimidazole fungicides

An Environmental and Economic Assessment for Selecting the Optimal Ground Heat Exchanger by Considering the Entering Water Temperature

In order to solve environmental problems such as global warming and resource depletion in the construction industry, interest in new renewable energy (NRE) systems has increased. The ground source heat pump (GSHP) system is the most efficient system among NRE systems. However, since the initial investment cost of the GSHP is quite expensive, a feasibility study needs to be conducted from the life-cycle perspective. Meanwhile, the efficiency of GSHP depends most significantly on the entering water temperature (EWT) of the ground heat exchanger (GHE). Therefore, this study aims to assess the environmental and economic effects of the use of GHE for selecting the optimal GHE. This study was conducted in three steps: (i) establishing the basic information and selecting key factors affecting GHE performances; (ii) making possible alternatives of the GHE installation by considering EWT; and (iii) using life-cycle assessment and life-cycle cost, as well as comprehensive evaluation of the environmental and economic effects on the GHE. These techniques allow for easy and accurate determination of the optimal design of the GHE from the environmental and economic effects in the early design phase. In future research, a multi-objective decision support model for the GSHP will be developed

Skin Lesion Classification Using Hybrid Convolutional Neural Network with Edge, Color, and Texture Information

Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 different features were extracted based on the edge, color, and texture features of the skin lesion image. For the edge features, a line-segment-type analysis algorithm was used, wherein the visual information of a dermoscopic image was precisely analyzed in terms of the units of pixels and was transformed into a structured pattern. Regarding the color features of skin lesions, the dermoscopic image was transformed into multiple color models, and the features were acquired by analyzing histograms showing information regarding the distribution of pixel intensities. Subsequently, texture features were extracted by applying the well-known Law’s texture energy measure algorithm. Feature data (50 × 256) generated via the feature extraction process above were used to classify skin lesions via a one-dimensional (1D) convolution layer-based classification model. Because the architecture of the designed model comprises parallel 1D convolution layers, fine-grained features of the dermoscopic image can be identified using different parameters. To evaluate the performance of the proposed method, datasets from the 2017 and 2018 International Skin Imaging Collaboration were used. A comparison of results yielded by well-known classification models and other models reported in the literature show the superiority of the proposed model. Additionally, the proposed method achieves an accuracy exceeding 88%

Sensitivity Analysis on the Impact Factors of the GSHP System Considering Energy Generation and Environmental Impact Using LCA

The world is facing a crisis due to energy depletion and environmental pollution. The ground source heat pump (GSHP) system, the most efficient new/renewable energy (NRE) system that can reduce the load of heating/cooling equipment in a building, can be used to address this crisis. Designers and contractors have implemented such systems depending on their experience, although there are many factors that affect the performance of the GSHP system. Therefore, this study aimed to conduct a sensitivity analysis on the impact factors in terms of energy generation and environmental impact. This study was conducted as follows: (i) collecting the impact factors that affect the GSHP system’s performance; (ii) establishing the GSHP system’s scenarios with the impact factors; (iii) determining the methodology and calculation tool to be used for conducting sensitivity analysis; and (iv) conducting sensitivity analysis on the impact factors of the GSHP system in terms of energy generation and environmental impact using life cycle assessment. The results of this study can be used: (i) to establish the optimal design strategy for different application fields and different seasons; and (ii) to conduct a feasibility study on energy generation and environmental impact at the level of the life cycle

Enhancing REST API Testing with NLP Techniques

RESTful services are commonly documented using OpenAPI specifications. Although numerous automated testing techniques have been proposed that leverage the machine-readable part of these specifications to guide test generation, their human-readable part has been mostly neglected. This is a missed opportunity, as natural language descriptions in the specifications often contain relevant information, including example values and inter-parameter dependencies, that can be used to improve test generation. In this spirit, we propose NLPtoREST, an automated approach that applies natural language processing techniques to assist REST API testing. Given an API and its specification, NLPtoREST extracts additional OpenAPI rules from the human-readable part of the specification. It then enhances the original specification by adding these rules to it. Testing tools can transparently use the enhanced specification to perform better test case generation. Because rule extraction can be inaccurate, due to either the intrinsic ambiguity of natural language or mismatches between documentation and implementation, NLPtoREST also incorporates a validation step aimed at eliminating spurious rules. We performed studies to assess the effectiveness of our rule extraction and validation approach, and the impact of enhanced specifications on the performance of eight state-of-the-art REST API testing tools. Our results are encouraging and show that NLPtoREST can extract many relevant rules with high accuracy, which can in turn significantly improve testing tools' performanc