Automated Test Input Generation for Android: Are We There Yet?

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android

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

CyTRANSFINDER: a Cytoscape 3.3 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction

Background: Biological research increasingly relies on network models to study complex phenomena. Signal Transduction Pathways are molecular circuits that model how cells receive, process, and respond to information from the environment providing snapshots of the overall cell dynamics. Most of the attempts to reconstruct signal transduction pathways are limited to single regulator networks including only genes/proteins. However, networks involving a single type of regulator and neglecting transcriptional and post-transcriptional regulations mediated by transcription factors and microRNAs, respectively, may not fully reveal the complex regulatory mechanisms of a cell. We observed a lack of computational instruments supporting explorative analysis on this type of three-component signal transduction pathways. Results: We have developed CyTRANSFINDER, a new Cytoscape plugin able to infer three-component signal transduction pathways based on user defined regulatory patterns and including miRNAs, TFs and genes. Since CyTRANSFINDER has been designed to support exploratory analysis, it does not rely on expression data. To show the potential of the plugin we have applied it in a study of two miRNAs that are particularly relevant in human melanoma progression, miR-146a and miR-214. Conclusions: CyTRANSFINDERsupportsthereconstructionofsmallsignaltransductionpathwaysamonggroupsof genes. Results obtained from its use in a real case study have been analyzed and validated through both literature data and preliminary wet-lab experiments, showing the potential of this tool when performing exploratory analysi

A computational pipeline to identify new potential regulatory motifs in melanoma progression

Molecular biology experiments allow to obtain reliable data about the expression of different classes of molecules involved in several cellular processes. This information is mostly static and does not give much clue about the causal relationships (i.e., regulation) among the different molecules. A typical scenario is the presence of a set of modulated mRNAs (up or down regulated) along with an over expression of one or more small non-coding RNAs molecules like miRNAs. To computationally identify the presence of transcriptional or post-transcriptional regulatory modules between one or more miRNAs and a set of target modulated genes, we propose a computational pipeline designed to integrate data from multiple online data repositories. The pipeline produces a set of three types of putative regulatory motifs involving coding genes, intronic miRNAs, and transcription factors. We used this pipeline to analyze the results of a set of expression experiments on a melanoma cell line that showed an over expression of miR-214 along with the modulation of a set of 73 other genes. The results suggest the presence of 27 putative regulatory modules involving miR-214, NFKB1, SREBPF2, miR-33a and 9 out of the 73 miR-214 modulated genes (ALCAM, POSTN, TFAP2A, ADAM9, NCAM1, SEMA3A, PVRL2, JAG1, EGFR1). As a preliminary experimental validation we focused on 9 out of the 27 identified regulatory modules that involve miR-33a and SREBF2. The results confirm the importance of the predictions obtained with the presented computational approach

Mechanical ventilation weaning issues can be counted on the fingers of just one hand: part 1

Although mechanical ventilation may be a patient's vital ally during acute illness, it can quickly transform into an enemy during chronic conditions. The weaning process is the fundamental phase that enables the resumption of physiological respiratory function; however, it is also associated with a number of life-threatening complications, and a large percentage of critically ill patients never achieve airway device removal or require the resumption of mechanical ventilation just a few days post-weaning. Indeed, the weaning process is, at present, more of an art than a science. As such, there is urgent need for novel contributions from the scientific literature to abate the growing rates of morbidity and mortality associated with weaning failure. The physician attempting to wean a patient must integrate clinical parameters and common-sense criteria. Numerous studies have striven to identify single predictive factors of weaning failure and sought to standardize the weaning process, but the results are characterized by remarkable heterogeneity. Despite the lack of benchmarks, it is clear that the analysis of respiratory function must include a detailed overview of the five situations described below rather than a single aspect. The purpose of this two-part review is to provide a comprehensive description of these situations to clarify the "arena" physicians are entering when weaning critically ill patients from mechanical ventilation

Mechanical ventilation weaning issues can be counted on the fingers of just one hand: part 2

Assessing heart and diaphragm function constitutes only one of the steps to consider along the weaning path. In this second part of the review, we will deal with the more systematic evaluation of the pulmonary parenchyma-often implicated in the genesis of respiratory failure. We will also consider the other possible causes of weaning failure that lie beyond the cardio-pulmonary-diaphragmatic system. Finally, we will take a moment to consider the remaining unsolved problems arising from mechanical ventilation and describe the so-called protective approach to parenchyma and diaphragm ventilation

An Informatics Framework for Testing Data Integrity and Correctness of Federated Biomedical Databases

Clinical research is increasingly relying on information gathered and managed in different database systems and institutions. Distributed data collection and management processes in such settings can be extremely complex and lead to a range of issues involving the integrity and accuracy of the distributed data. To address this challenge, we propose a middleware framework for assessing the data integrity and correctness in federated environments. The framework has two main elements: (1) a test model describing the dependencies between and constraints on data sources and datasets, and (2) a family of testing techniques that create and execute test cases based on the model