Software trustworthiness evaluation model based on a behaviour trajectory matrix

Tian, Junfeng; Guo, Yuhui

doi:10.1016/j.infsof.2019.106233

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…They also have conducted numerous experiments in a large-scale real QoS datasets, and the experimental results demonstrate the effectiveness of their method. Tian et al build a software trustworthiness evaluation model based on a behavior trajectory matrix [15]. Checkpoints are set in the software behavior trajectory, and binary code is introduced to represent the software behavior trajectory tree.…”

Section: Related Workmentioning

confidence: 99%

“…The essence of this method is to predict the trustworthiness of attributes according to the internal attribute values. Typical models for the synthesis of trustworthy attribute values include machine learning [11][12][13][14][15], axiomatic approaches [16][17][18][19][20], uncertainty theory [21][22][23][24], system testing [25], the social-to-software framework [26], user feedback [27], heuristic-systematic processing [28], the development framework [29], crowd wisdom [30], etc.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Software Trustworthiness Evaluation Strategy via Relationships between Criteria

et al. 2022

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Software trustworthiness evaluation is regarded as a multi-criteria decision-making problem. However, most current software trustworthiness evaluation methods do not consider the relationships between criteria. In this paper, we present a software trustworthiness evaluation strategy via the relationships between criteria. Because the trustworthy attribute degree is evaluated by a criterion, a trustworthy attribute measurement method based on fuzzy theory is first proposed, and the relationships between criteria are described by cooperative and conflicting degrees between criteria. Then, a measure formula for the symmetric substitutivity between criteria is proposed, and the cooperative degree between criteria is taken as the approximation of the symmetric substitutivity between criteria. With the help of the symmetric substitutivity between criteria, the software trustworthiness measurement model obtained by axiomatic approaches is applied to aggregate the degree to which each optional software product meets each objective. Finally, the candidate software products are sorted according to the trustworthiness aggregation results, and the optimal product is obtained from the alternative software products on the basis of the sorting results. The theoretical rationality of the measurement model is validated by proving that it satisfies the desirable properties of software trustworthiness measures, and its effectiveness is demonstrated through a case study.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Software Trustworthiness Evaluation Strategy via Relationships between Criteria

et al. 2022

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“…Meanwhile, scholars have achieved many research results in the measurement of software security and credibility. From software requirement [24], behavior [25,26], capability [27], and software development process [28], software credibility analysis, modeling, and measurement are carried out. e method of multiattribute decision making is widely used in engineering, technology, and economy and is also used in software credibility analysis [29,30].…”

Section: Related Workmentioning

confidence: 99%

Risk Measurement Method of Collusion Privilege Escalation Attacks for Android Apps Based on Feature Weight and Behavior Determination

Shen

Wang

et al. 2021

Security and Communication Networks

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To solve the issue of measuring the risk of the application-layer collusion privilege escalation attacks in Android apps, this paper proposed a risk measurement method based on the feature weight and behavior determination. Analytic hierarchy process (AHP) is used to calculate the weight of feature in the feature set extracted from the app. App behavior and attack behavior are modeled by process algebra. The weak equivalent and nonequivalent are introduced to determine the behavior of apps, whereas the measurement function is constructed to calculate the app risk measurement value. In an experiment with three known apps, the measurement values are 0.629, 1, and 0.976. These results are consistent with reality, and the effectiveness and feasibility of the proposed method are verified. Through the benchmark and test set experiments, it can be seen that the measurement value of apps that has weak equivalent to attack behavior is distributed between 0.0468 and 1, and the measurement value distribution is reasonable, which verifies the accuracy and rationality of the method.

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“…It is essential and necessary to evaluate software trustworthiness based on the influence degrees of different software behaviors for minimizing the interference of human factors. Tian et al [109] constructed behaviour trajectory matrices to represent the behaviour trajectory and then trained the deep residual network (ResNet) as a software trustworthiness evaluation model to classify the current software behaviors. After that, they used the cosine similarity algorithm to calculate the deviation degree of the software behavior trajectory.…”

Section: Software Maintenancementioning

confidence: 99%

A Survey on Deep Learning for Software Engineering

Yang¹,

Xia²,

Lo³

et al. 2020

Preprint

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In 2006, Geoffrey Hinton proposed the concept of training "Deep Neural Networks (DNNs)" and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in SE. Few works to date focus on summarizing, classifying, and analyzing the application of deep learning techniques in SE. To fill this gap, we performed a survey to analyse the relevant studies published since 2006. We first provide an example to illustrate how deep learning techniques are used in SE. We then summarize and classify different deep learning techniques used in SE. We analyzed key optimization technologies used in these deep learning models, and finally describe a range of key research topics using DNNs in SE. Based on our findings, we present a set of current challenges remaining to be investigated and outline a proposed research road map highlighting key opportunities for future work.

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Software trustworthiness evaluation model based on a behaviour trajectory matrix

Cited by 10 publications

References 3 publications

A Novel Software Trustworthiness Evaluation Strategy via Relationships between Criteria

A Novel Software Trustworthiness Evaluation Strategy via Relationships between Criteria

Risk Measurement Method of Collusion Privilege Escalation Attacks for Android Apps Based on Feature Weight and Behavior Determination

A Survey on Deep Learning for Software Engineering

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