Software effort estimation (SEE) models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in three software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of non-uniform weights which are subsequently employed in weighted linear regression modeling. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for datasets that exhibit underlying non-stationary processes, uniform models are more accurate than non-uniform models. In contrast, the accuracy of uniform and non-uniform models for datasets that exhibited stationary processes was essentially equivalent. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.
Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weight…
To effectively test parts of the Internet of Things (IoT) systems with a state machine character, Model-based Testing (MBT) approach can be taken. In MBT, a system model is created, and test cases are generated automatically from the model, and a number of current strategies exist. In this paper, we propose a novel alternative strategy that concurrently allows us to flexibly adjust the preferred length of the generated test cases, as well as to mark the states, in which the test case can start and end. Compared with an intuitive N-switch coverage-based strategy that aims at the same goals, our…
Model-based testing is a promising technology for black-box software and hardware testing, in which test cases are generated automatically from high-level specifications. Nowadays, systems typically consist of multiple interacting components and, due to their complexity, testing presents a considerable portion of the effort and cost in the design process. Exploiting the compositional structure of system specifications can considerably reduce the effort in model-based testing. Moreover, inferring properties about the system from testing its individual components allows the designer to reduce th…
Testing of software or software-based systems and services is considered as one of the most effort-consuming activities in the lifecycle. This applies especially to those domains where highly iterative development and continuous integration cannot be applied. Several approaches have been proposed to use measurement as a means to improve test effectiveness and efficiency. Most of them rely on using product data, historical data, or in-process data that is not related to quality assurance ac- tivities. Very few approaches use data from early quality assurance activities such as inspection data i…
As software systems are becoming larger, more complex, and dependent on many third-party software components, the chances of their failure are increasing further. This calls for intense efforts to improve the quality of testing in the software development process.
Deep learning (DL) systems are increasingly applied to safety-critical domains such as autonomous driving cars. It is of significant importance to ensure the reliability and robustness of DL systems. Existing testing methodologies always fail to include rare inputs in the testing dataset and exhibit low neuron coverage. In this paper, we propose DLFuzz, the frst differential fuzzing testing framework to guide DL systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated …
When it comes to industrial organizations, current collaboration efforts in software engineering research are very often kept in-house, depriving these organizations off the skills necessary to build independent collaborative research. The current trend, towards empirical software engineering research, requires certain standards to be established which would guide these collaborative efforts in creating a strong partnership that promotes independent, evidence-based, software engineering research. This paper examines key enabling factors for an efficient and effective industry-academia collabor…
The pivotal role of testing in high-quality software production has driven a significant effort in evaluating and assessing testing practices. We explore the state of testing in a large industrial project over an extended period. We study the interplay between bugs in the project and its test cases, and interview developers and stakeholders to uncover reasons underpinning our observations. We realized that testing is not well adopted, and that testability (ie, ease of testing) is low. We found that developers tended to abandon writing tests when they assessed the effort to be high. Frequent ch…
We propose and demonstrate a method for the reduction of testing effort in safety-critical software development using DO-178 guidance. We achieve this through the application of Bounded Model Checking (BMC) to formal low-level requirements, in order to generate tests automatically that are good enough to replace existing labor-intensive test writing procedures while maintaining independence from implementation artefacts. Given that existing manual processes are often empirical and subjective, we begin by formally defining a metric, which extends recognized best practice from code coverage anal…
Infrastructure as a Service (IaaS) Cloud services allow users to deploy distributed applications in a virtualized environment without having to customize their applications to a specific Platform as a Service (PaaS) stack. It is common practice to host multiple Virtual Machines (VMs) on the same server to save resources. Traditionally, IaaS data center management required manual effort for optimization, e.g. by consolidating VM placement based on changes in usage patterns. Many resource management algorithms and frameworks have been developed to automate this process. Resource management algor…
Context: In addressing how best to estimate how much effort is required to develop software, a recent study found that using exemplary and recently completed projects [forming Bellwether moving windows (BMW)] in software effort prediction (SEP) models leads to relatively improved accuracy. More studies need to be conducted to determine whether the BMW yields improved accuracy in general, since different sizing and aging parameters of the BMW are known to affect accuracy. Objective: To investigate the existence of exemplary projects (Bellwethers) with defined window size and age parameters, and…
Automotive engineering is recognized as a combination of software and mechanical engineering due to the ever-increasing number of software-based components in vehicles. Since vehicles have become more sophisticated than before to ensure robustness, testing of automotive electronics is performed in high volume, producing immense test-related data. This study investigates how unstructured and decentralized test-related data from testing of automotive electronics creates issues in decision making during the testing and analysis process of test artifacts by performing an exploratory case-study at …
The workshop is devoted to model-based testing of both software and hardware. Model-based testing uses models describing the required behavior of the system under consideration to guide such efforts as test selection and test results evaluation. Testing validates the real system behavior against models and checks that the implementation conforms to them, but is capable also to find errors in the models themselves. The intent of this workshop is to bring together researchers and users of model-based testing techniques and tools to discuss the state of the art in theory, applications, tools, and…
PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur（PDPM印度信息技术设计与制造学院贾巴尔普尔）
Reducing the effort required to make changes in web services is one of the primary goals in web service projects maintenance and evolution. Normally, functional and non-functional testing of a web service is performed by testing the operations specified in its WSDL. The regression testing is performed by identifying the changes made thereafter to the web service code and the WSDL. In this thesis, we present a tool-supported approach to perform efficient regression testing of web services. By representing a web service as a directed graph of WSDL elements, we identify and gathers the changed po…
Random testing (RT) is a black-box software testing technique that tests programs by generating random test inputs. It is a widely used technique for software quality assurance, but there has been much debate by practitioners concerning its failure-detection effectiveness. RT is argued to be possibly less effective by some researchers as it does not utilize any information about the program under test. Efforts to mainly improve the failure-detection capability of RT, have led to the proposition of Adaptive Random Testing (ART). ART takes advantage of the location information of previous non-fa…
The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms…
Integrating inspection processes with testing processes promises to deliver several benefits, including reduced effort for quality assurance or higher defect detection rates. Systematic integration of these processes requires knowledge regarding the relationships between these processes, especially regarding the relationship between inspection defects and test defects. Such knowledge is typically context-dependent and needs to be gained analytically or empirically. If such kind of knowledge is not available, assumptions need to be made for a specific context. This article describes the relevan…
Software testing is an expensive and important task. Plenty of researches and industrial efforts have been invested on improving software testing techniques, including criteria, tools, etc. These studies can provide guidelines to select suitable test techniques for software engineers. However, in some engineering projects, business issues may be more important than technical ones, hence we need to lobby non-technical members to support our decisions. In this paper, a well-known investment model, Nelson-Siegel model, is introduced to evaluate and forecast the processes of testing with different…
Context: Search-based software testing promises to provide users with the ability to generate high-quality test cases, and hence increase product quality, with a minimal increase in the time and effort required. One result that emerged out of a previous study to investigate the application of search-based software testing (SBST) in an industrial setting was the development of the Interactive Search-Based Software Testing (ISBST) system. ISBST allows users to interact with the underlying SBST system, guiding the search and assessing the results. An industrial evaluation indicated that the ISBST…
Integration testing is one the important phase in software testing life cycle (STLC). With the fast growth of internet and web services, web-based applications are also growing rapidly and their importance and complexity is also increasing. Heterogeneous and diverse nature of distributed components, applications, along with their multi-platform support and cooperativeness make these applications more complex and swiftly increasing in their size. Quality assurance of these applications is becoming more crucial and important. Testing is one of the key processes to achieve and ensure the quality …
Software testing has often to be done under severe pressure due to limited resources and a challenging time schedule facing the demand to assure the fulfillment of the software requirements. In addition, testing should unveil those software defects that harm the mission-critical functions of the software. Risk-based testing uses risk (re-)assessments to steer all phases of the test process in order to optimize testing efforts and limit risks of the software-based system. Due to its importance and high practical relevance several risk-based testing approaches were proposed in academia and indus…
During acceptance testing customers assess whether a system meets their expectations and often identify issues that should be improved. These findings have to be communicated to the developers a task we observed to be error prone, especially in distributed teams. Here, it is normally not possible to have developer representatives from every site attend the test. Developers who were not present might misunderstand insufficiently documented findings. This hinders fixing the issues and endangers customer satisfaction. Integrated feedback systems promise to mitigate this problem. They allow to eas…
Objective: This paper investigates the potential of ensemble learning for variants of adjustment methods used in analogy-based effort estimation. The number k of analogies to be used is also investigated. Method We perform a large scale comparison study where many ensembles constructed from n out of 40 possible valid variants of adjustment methods are applied to eight datasets. The performance of each method was evaluated based on standardized accuracy and effect size. Results: The results have been subjected to statistical significance testing, and show reasonable significant improvements on …
This volume contains the proceedings of the Eighth Workshop on Model-Based Testing (MBT 2013), which was held on March 17, 2013 in Rome, Italy, as a satellite event of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013. The workshop is devoted to model-based testing of both software and hardware. Model-based testing uses models describing the required behavior of the system under consideration to guide such efforts as test selection and test results evaluation. Testing validates the real system behavior against models and checks that the implementation conforms to th…
Estimating the effort of software systems is an essential topic in software engineering, carrying out an estimation process reliably and accurately for a software forms a vital part of the software development phases. Many researchers have utilized different methods and techniques hopping to find solutions to this issue, such techniques include COCOMO, SEER-SEM,SLIM and others. Recently, Artificial Intelligent techniques are being utilized to solve such problems; different studies have been issued focusing on techniques such as Neural Networks NN, Genetic Algorithms GA, and Genetic Programming…
Formal methods and testing are two important approaches that assist in the development of high quality software. For long time these approaches have been seen as competitors and there was very little interaction between the two communities. In recent years a new consensus has developed in which they are seen as more complementary. In this report we present an approach based on the ASD(Analytical Software Design) suite by Verum and the Microsoft Spec Explorer Model Based Testing(MBT) tool. ASD is a model-based design approach that can produce verified software components that can be combined in…
Specific testing solutions targeting blockchain-based software are gaining huge attention as blockchain technologies are being increasingly incorporated into enterprise systems. As blockchain-based software enters production systems, it is paramount to follow proper engineering practices, ensure the required level of testing, and assess the readiness of the developed system. The existing research aims at addressing the testing-related issues and challenges of engineering blockchain-based software by providing suitable techniques and tools. However, like any emerging discipline, the best practi…
A major challenge in testing software product lines is efficiency. In particular, testing a product line should take less effort than testing each and every product individually. We address this issue in the context of input-output conformance testing, which is a formal theory of model-based testing. We extend the notion of conformance testing on input-output featured transition systems with the novel concept of spinal test suites. We show how this concept dispenses with retesting the common behavior among different, but similar, products of a software product line.
Technical University of Munich（慕尼黑工业大学）；Siemens Technology（西门子科技）；LMU Munich（慕尼黑大学）
Over the last years, software development in domains with high security demands transitioned from traditional methodologies to uniting modern approaches from software development and operations (DevOps). Key principles of DevOps gained more importance and are now applied to security aspects of software development, resulting in the automation of security-enhancing activities. In particular, it is common practice to use automated security testing tools that generate reports after inspecting a software artifact from multiple perspectives. However, this raises the challenge of generating duplicat…
Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
SEED - Electronic Arts (EA)（SEED——美国艺电（EA））；Università degli Studi di Firenze（佛罗伦萨大学）
In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible. This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We investigate the validity of our approach through a user study with industry experts. The survey results show that our method is indeed …
Graz University of Technology（格拉茨工业大学）；Siemens Energy AG（西门子能源股份公司）；University of Sevilla（塞维利亚大学）
Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DirectDebug which is a direct diagnosis approach to the automated testing and debugging of variabi…
Testing practices within the machine learning (ML) community have centered around assessing a learned model's predictive performance measured against a test dataset, often drawn from the same distribution as the training dataset. While recent work on robustness and fairness testing within the ML community has pointed to the importance of testing against distributional shifts, these efforts also focus on estimating the likelihood of the model making an error against a reference dataset/distribution. We argue that this view of testing actively discourages researchers and developers from looking …
RISE Research Institutes of Sweden（瑞典RISE研究院）；Mälardalen University（梅拉达伦大学）
Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead cou…
