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Collaborative Application Security Testing for DevSecOps An Empirical Analysis of Challenges, Best Practices and Tool Support

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arXiv 2022年

作者： Rajapakse, Roshan Namal Zahedi, Mansooreh Babar, Muhammad Ali Centre for Research on Engineering Software Technologies School of Computer Science The University of Adelaide Australia Cyber Security Cooperative Research Centre Australia School of Computing and Information Systems The University of Melbourne Australia

DevSecOps is a software development paradigm that places a high emphasis on the culture of collaboration between developers (Dev), security (Sec) and operations (Ops) teams to deliver secure software continuously and rapidly. Adopting this paradigm effectively, therefore, requires an understanding of the challenges, best practices and available solutions for collaboration among these functional teams. However, collaborative aspects related to these teams have received very little empirical attention in the DevSecOps literature. Hence, we present a study focusing on a key security activity, Application Security Testing (AST), in which practitioners face difficulties performing collaborative work in a DevSecOps environment. Our study made novel use of 48 systematically selected webinars, technical talks and panel discussions as a data source to qualitatively analyse practitioner discussions on the most recent trends and emerging solutions in this highly evolving field. We find that the lack of features that facilitate collaboration built into the AST tools themselves is a key tool-related challenge in DevSecOps. In addition, the lack of clarity related to role definitions, shared goals, and ownership also hinders Collaborative AST (CoAST). We also captured a range of best practices for collaboration (e.g., Shift-left security), emerging communication methods (e.g., ChatOps), and new team structures (e.g., hybrid teams) for CoAST. Finally, our study identified several requirements for new tool features and specific gap areas for future research to provide better support for CoAST in DevSecOps. © 2022, CC BY.

关键词： software testing

Energy-aware data processing techniques for wireless sensor networks: A review

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Energy-aware data processing techniques for wireless sensor ...

3rd International Conference on Data Management in Grid and Peer-to-Peer systems, Globe 2010

作者： Chong, Suan Khai Gaber, Mohamed Medhat Krishnaswamy, Shonali Loke, Seng Wai Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield EastVIC3145 Australia School of Computing University of Portsmouth Portsmouth HampshirePO1 3HE United Kingdom Department of Computer Science and Computer Engineering La Trobe University VIC3086 Australia

ISBN: (纸本)9783642230738

Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve this goal on sensor nodes are required to operate while meeting resource constraints such as memory and power to prolong a sensor network’s lifetime. This survey serves to provide a comprehensive examination of such techniques, enabling developers of WSN applications to select and implement data processing techniques that perform efficiently for their intended WSN application. It presents a general analysis of the issue of energy conservation in sensor networks and an up-to-date classification and evaluation of data processing techniques that have factored in energy constraints of sensors. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Sensor nodes

Mitigating ML Model Decay in Continuous Integration with Data Drift Detection: An Empirical Study

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arXiv 2023年

作者： Arani, Ali Kazemi Babar, Muhammad Ali Le, Triet Huynh Minh Zahedi, Mansooreh CREST-The Centre for Research on Engineering Software Technologies School of Computer Science University of Adelaide Adelaide Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

Background: Machine Learning (ML) methods are being increasingly used for automating different activities, e.g., Test Case Prioritization (TCP), of Continuous Integration (CI). However, ML models need frequent retraining as a result of changes in the CI environment, more commonly known as data drift. Also, continuously retraining ML models consume a lot of time and effort. Hence, there is an urgent need of identifying and evaluating suitable approaches that can help in reducing the retraining efforts and time for ML models used for TCP in CI environments. Aims: This study aims to investigate the performance of using data drift detection techniques for automatically detecting the retraining points for ML models for TCP in CI environments without requiring detailed knowledge of the software projects. Method: We employed the Hellinger distance to identify changes in both the values and distribution of input data, and leveraged these changes as retraining points for the ML model. We evaluated the efficacy of this method on multiple datasets and compared the APFDc and NAPFD evaluation metrics against models that were regularly retrained, with careful consideration of the statistical methods. Results: Our experimental evaluation of the Hellinger distance-based method demonstrated its efficacy and efficiency in detecting retraining points and reducing the associated costs. However, the performance of this method may vary depending on the dataset. Conclusions: Our findings suggest that data drift detection methods can assist in identifying retraining points for ML models in CI environments, while significantly reducing the required retraining time. These methods can be helpful for practitioners who lack specialized knowledge of software projects, enabling them to maintain ML model accuracy. © 2023, CC BY.

关键词： Transmission control protocol

SYSTEMATIC LITERATURE REVIEW ON APPLICATION OF LEARNING-BASED APPROACHES IN CONTINUOUS INTEGRATION

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arXiv 2024年

作者： Arani, Ali Kazemi Le, Triet Huynh Minh Zahedi, Mansooreh Babar, M. Ali CREST-The Centre for Research on Engineering Software Technologies University of Adelaide AdelaideSA5005 Australia School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia

Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods, while the growing adoption of such approaches underscores the need for systematizing knowledge. Objective: Our objective is to comprehensively review and analyze existing literature concerning learning-based methods within the CI domain. We endeavour to identify and analyse various techniques documented in the literature, emphasizing the fundamental attributes of training phases within learning-based solutions in the context of CI. Method: We conducted a Systematic Literature Review (SLR) involving 52 primary studies. Through statistical and thematic analyses, we explored the correlations between CI tasks and the training phases of learning-based methodologies across the selected studies, encompassing a spectrum from data engineering techniques to evaluation metrics. Results: This paper presents an analysis of the automation of CI tasks utilizing learning-based methods. We identify and analyze nine types of data sources, four steps in data preparation, four feature types, nine subsets of data features, five approaches for hyperparameter selection and tuning, and fifteen evaluation metrics. Furthermore, we discuss the latest techniques employed, existing gaps in CI task automation, and the characteristics of the utilized learning-based techniques. Conclusion: This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation. It also highlights the need for further research to advance these methods in CI. © 2024, CC BY.

关键词： Automation

Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings

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Data-Driven Distributionally Robust Safety Verification Usin...

American Control Conference (ACC)

作者： Oliver Schön Zhengang Zhong Sadegh Soudjani School of Computing Newcastle University Newcastle upon Tyne United Kingdom Sargent Centre for Process Systems Engineering Imperial College London London United Kingdom Max Planck Institute for Software Systems Kaiserslautern Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Algorithmic verification of realistic systems to sat-isfy safety and other temporal requirements has suffered from poor scalability of the employed formal approaches. To design systems with rigorous guarantees, many approaches still rely on exact models of the underlying systems. Since this assumption can rarely be met in practice, models have to be inferred from measurement data or are bypassed completely. Whilst former usually requires the model structure to be known a-priori and immense amounts of data to be available, latter gives rise to a plethora of restrictive mathematical assumptions about the unknown dynamics. In a pursuit of developing scalable formal verification algorithms without shifting the problem to unrealistic assumptions, we employ the concept of barrier certificates, which can guarantee safety of the system, and learn the certificate directly from a compact set of system trajectories. We use conditional mean embeddings to embed data from the system into a reproducing kernel Hilbert space (RKHS) and construct an RKHS ambiguity set that can be inflated to robustify the result w.r.t. a set of plausible transition kernels. We show how to solve the resulting program efficiently using sum-of-squares optimization and a Gaussian process envelope. Our approach lifts the need for restrictive assumptions on the system dynamics and uncertainty, and suggests an improvement in the sample complexity of verifying the safety of a system on a tested case study compared to a state-of-the-art approach.

关键词： Uncertainty System dynamics Scalability Mathematical models Data models Hilbert space Safety

SYSTEMATIC LITERATURE REVIEW ON APPLICATION OF MACHINE LEARNING IN CONTINUOUS INTEGRATION

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arXiv 2023年

作者： Arani, Ali Kazemi Le, Triet Huynh Minh Zahedi, Mansooreh Babar, Muhammad Ali CREST-The Centre for Research on Engineering Software Technologies School of Computer Science University of Adelaide AdelaideSA Australia School of Computing and Information Systems University of Melbourne MelbourneVIC Australia

Context: Machine learning (ML) is a field that involves analysing raw data and extracting useful information from it through specific phases. As continuous practices become more prevalent in software projects, there is a need to explore how ML methods can be trained to enhance the Continuous Integration (CI) pipeline. Moreover, the growing utilization of ML algorithms in CI, combined with the surge of literature on the subject, highlights the significance of establishing a comprehensive body of knowledge to support future researchers in conducting high-quality research and bridging any existing gaps. Objective: The objective of this research is to conduct a systematic review and analysis of the existing literature on ML-based methods employed in the CI domain. This study aims to identify and describe the various techniques employed in the literature and present the key characteristics of the training phases of ML-based solutions in the CI context. Method: To achieve this objective, we conducted a Systematic Literature Review (SLR) of 48 primary studies that were selected after searching relevant literature published over the past 22 years (2000–November 2022). We used statistical and thematic analysis to examine the composition phase of CI, data engineering techniques and data source types, feature engineering methods and extracted features, the employed hyper-parameter tuning methods and types of the ML models, and the evaluation methods and metrics used in the selected studies. Additionally, this article aims to present the relationship between these concepts. Results: In this paper, we have depicted the phases of CI testing, the connection between them, and the employed techniques in training the ML method phases. We presented nine types of data sources and four taken steps in the selected studies for preparing the data. Also, we identified four feature types and nine subsets of data features through thematic analysis of the selected studies. Besides, five method

关键词： Machine learning

A suite of domain-specific visual languages for scientific software application modelling

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A suite of domain-specific visual languages for scientific s...

IEEE Symposium on Visual Languages and Human Centric computing (VL/HCC)

作者： Mohamed Almorsy John Grundy Richard Sadus Willem van Straten David G. Barnes Owen Kaluza Centre for Computing and Engineering Software and Systems Swinburne University of Technology Hawthorn Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn Australia Monash Monash Biomedical Imaging and Monash e-Research Centre Monash University Clayton Australia

ISBN: (纸本)9781479903702

Many advances in science now require sophisticated scientific software applications that facilitate data and computationally intensive experiments. However, the effective utilization of existing computational power e.g., grid and cloud platforms depends on the capabilities of scientists to implement parallel, scalable code for such experiments. Currently, tools aimed at supporting scientists are either very limited to specific domains, or require significant development using low-level code. We describe our work towards a more end user-friendly scientific applications development process, notations and toolset. We introduce a scientific application designer intended for use primarily by scientists to enable them in describing workflow, processes, entities, formulae, computation and ultimately realization code for different computing platforms. This is achieved via a set of integrated, domain-specific visual and textual languages (DSVLs). A Web-based modeling tool supports definition of new DSVLs and modeling of these applications. We are currently extending our tool to support generation of multi-core and GPU implementations, and visualization of results.

关键词： Computational modeling Visualization Prototypes Graphics processing units Programming Mathematical model

An Algorithm for Cost-Effectively Storing Scientific Datasets with Multiple Service Providers in the Cloud

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An Algorithm for Cost-Effectively Storing Scientific Dataset...

IEEE International Conference on e-Science and Grid computing

作者： Dong Yuan Xiao Liu Lizhen Cui Tiantian Zhang Wenhao Li Dahai Cao Yun Yang Centre for Computing Engineering and Software Systems Swinburne University of Technology Melbourne Australia Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute of East China Normal University Shanghai China School of Computer Science and Technology Shandong University Jinan China School of Computer Science and Technology Anhui University Hefei China

The proliferation of cloud computing allows scientists to deploy computation and data intensive applications without infrastructure investment, where large generated datasets can be flexibly stored with multiple cloud service providers. Due to the pay-as-you-go model, the total application cost largely depends on the usage of computation, storage and bandwidth resources, and cutting the cost of cloud-based data storage becomes a big concern for deploying scientific applications in the cloud. In this paper, we propose a novel algorithm that can automatically decide whether a generated dataset should be 1) stored in the current cloud, 2) deleted and re-generated whenever reused or 3) transferred to cheaper cloud service for storage. The algorithm finds the trade-off among computation, storage and bandwidth costs in the cloud, which are three key factors for the cost of storing generated application datasets with multiple cloud service providers. Simulations conducted with popular cloud service providers' pricing models show that the proposed algorithm is highly cost-effective to be utilised in the cloud.

关键词： Bandwidth Cloud computing Computational modeling Algorithm design and analysis Finite element analysis Data models Educational institutions

Development of Robust Traceability Benchmarks

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Development of Robust Traceability Benchmarks

Australian software engineering Conference

作者： Xiaofan Chen John Hosking John Grundy Robert Amor Department of Computer Science University of Auckland Auckland New Zealand College of Engineering & Computer Science Australian National University Canberra Australia Centre for Computing and Engineering Software Systems Swinburne University of Technology Hawthorn Australia

ISBN: (纸本)9781479902262

Traceability benchmarks are essential for the evaluation of traceability recovery techniques. This includes the validation of an individual traceability technique itself and the objective comparison of the technique with other traceability techniques. However, it is generally acknowledged that it is a real challenge for researchers to obtain or build meaningful and robust benchmarks. This is because of the difficulty of obtaining or creating suitable benchmarks. In this paper, we describe an approach to enable researchers to establish affordable and robust benchmarks. We have designed rigorous manual identification and verification strategies to determine whether or not a link is correct. We have developed a formula to calculate the probability of errors in benchmarks. Analysis of error probability results shows that our approach can produce high quality benchmarks, and our strategies significantly reduce error probability in them.

关键词： traceability benchmark benchmark development