ABSTRACT Background In recent years, machine learning (ML) APIs have emerged as a valuable resource for addressing complex problems, such as image recognition. However, developers should use ML APIs carefully, as they have their own characteristics different from traditional ones: an ML API has its own training data set, a concrete target task, and its output is often the probability. As a result, developers may use an inappropriate API, and the program can still run without reporting errors, especially as there are many similar ML APIs provided by different platforms. Methods This paper proposes an approach called DAOR to help developers use ML APIs properly in their tasks. First, a comparative analysis of ML APIs is conducted, leveraging information from documentation and user reviews to identify comparable APIs. This involves extracting differences from the documentation, categorized into inputs, functions, and outputs, and summarizing key information from user reviews using GPT‐driven prompts. Finally, a visualization framework is designed to summarize and show the results. Evaluation and Results To evaluate the approach, a series of experiments is conducted based on the ML APIs from two famous platforms, Amazon Web Service AI and IBM Watson. The results show that useful information for distinguishing similar ML APIs can be gained, and it is helpful for developers to use the ML APIs correctly.

Logic synthesis is a crucial step in integrated circuit design, and area optimization is an indispensable part of this process. However, the area optimization problem for large-scale Fixed Polarity Reed-Muller (FPRM) circuits is an NP-hard problem. To address this problem, we divide Boolean circuits into small-scale circuits based on the idea of divide-and-conquer using the proposed grouping decomposition mechanism. Each small-scale Boolean circuit is transformed into an FPRM circuit by a polarity transformation algorithm. To ensure the circuit’s functionality remains unaffected, we integrate FPRM circuits into an FPRM and Boolean (RM-TB) dual logic circuit based on the proposed gate-level integration. However, the area optimization problem of RM-TB dual logic circuits is a multi-task, high-dimensional, and multi-extremal combinatorial optimization problem. Therefore, we propose a Multipopulation Multitasking Optimization Algorithm (MMuOA) that integrates self-evolution with a multitasking equilibrium optimizer and cross-task evolution through knowledge sharing and transfer. This forms a dynamic optimization framework for simultaneously searching for the optimal polarity corresponding to the minimal area of RM-TB dual logic circuits. Moreover, we propose an Area Optimization Approach (AOA) for an RM-TB dual logic circuit with the minimum area using the MMuOA. Experimental results based on the Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the AOA compared to the state-of-the-art area optimization approach.

Malware detection is a critical issue in software engineering as it directly threatens user information security. Existing approaches often focus on individual modality (either source code or binary code) for the detection, but it ignores to effectively exploit the complementary information between them. This limits the detection performance, especially in complex and evasive malware scenarios. In this paper, we take Android applications written in Java as objects, and provide a novel fine-grained multimodal fusion method with large pre-trained models to combine the features from source and binary codes for the malware detection. For the source code modality, we employ the graphical user interface (GUI) as a framework to segment the source code into snippets, and use a pre-trained programming language model to extract feature representations. For the binary code modality, we convert binary code into grayscale images and fine-tune a pre-trained vision model to extract features indirectly. We then implement cross-modal attention and devise a contrastive loss to align features across modalities, supplementing this with supervised classification loss to refine the multimodal fusion process specifically for malware detection. Our experiments, conducted using the Data-MD and Data-MC benchmarks, demonstrate that our approach achieves a precision of 0.977 and a recall of 0.984 in detecting malware. This underscores the advantages of using large pre-trained models for feature representation and the fusion of information across different modalities for effective malware detection.

Logic synthesis is a crucial step in integrated circuit design, and power optimization is an indispensable part of this process. However, power optimization for large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits is an NP-hard problem. In this article, we divide Boolean circuits into small-scale circuits based on the idea of divide and conquer using the proposed Dynamic Adaptive Grouping Strategy (DAGS) and the proposed circuit decomposition model (CDM). Each small-scale Boolean circuit is transformed into an MPRM logic circuit by a polarity transformation algorithm. Based on the gate-level integration, we integrate small-scale circuits into an MPRM and Boolean Dual Logic (RBDL) circuit. Furthermore, the power optimization problem of RBDL circuits is a multi-task, multi-extremal, high-dimensional combinatorial optimization problem, for which we propose an Adaptive Multi-task Intelligent Algorithm (AMIA), which includes global task optimization, population reproduction, valuable knowledge transfer (VKT), and local exploration to search for the lowest power for RBDL circuits. Moreover, based on the proposed Fast Power Decomposition Algorithm (FPDA), we proposed a Power Optimization Approach (POA) for an RBDL circuit with the lowest power using the AMIA. Experimental results based on Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the POA compared to state-of-the-art POAes.

Links between issues and pull requests (PRs) assist GitHub developers in tackling technical challenges, gaining development inspiration, and improving repository maintenance. In realistic repositories, these links are still insufficiently established. Aiming at this situation, existing works focus on issues and PRs themselves and employ text similarity with additional information like issue size to predict issue-PR links, yet their effectiveness is unsatisfactory. The limitation is that issues and PRs are not isolated on GitHub. Rather, they are related to multiple GitHub sources, including repositories and submitters, which, through their diverse relationships, can supply potential and crucial knowledge about technical domains, developmental insights, and cross-repository technical details. To this end, we proposeAutoIPLinker (AIPL), which introduces the heterogeneous graph to model multiple GitHub sources with their relationships. Further, it leverages the metapath-based technique to reveal and incorporate the potential information for a more comprehensive understanding of issues and PRs. Firstly, we identify 4 types of GitHub sources related to issues and PRs (repositories, users, issues, PRs) as well as their relationships, and model them into task-specific heterogeneous graphs. Next, we analyze information transmitted among issues or PRs to reveal which knowledge is crucial for them. Based on our analysis, we formulate a series of metapaths and employ the metapath-based technique to incorporate various information for learning the knowledge-aware embedding of issues and PRs. Finally, we can infer whether an issue and a PR can be linked based on their embedding. We evaluate the performance of AIPL on real-world data sets collected from GitHub. The results show that, compared to the baselines, AIPL can achieve average improvements of 15.94&, 15.19&, 20.52&, and 18.50& in terms of Accuracy, Precision, Recall, and F1-score.

Filter pruning, a representative model compression technique , has been widely used to compress and accelerate sophisticated deep neural networks on resource-constrained platforms. Nevertheless, most studies focus on reducing the cost of model inference, whereas the heavy burden of the pruning optimization process is neglected. In this paper, we propose MaskACC, a mask-aware convolutional computation method, which accelerates the prevailing mask-based filter pruning process on modern CPU platforms. MaskACC dynamically reorganizes the tensors used in convolutions with the mask information to avoid unnecessary computations, thereby improving the computational efficiency of the pruning process. Evaluation with state-of-the-art neural network models on CPU cloud platforms demonstrates the effectiveness of our method, which achieves up to 1.61 × speedup under commonly-used pruning rates, compared to conventional computations.

Abstract With the rapid increase in the number of Apps, the requirement of users has also become extremely complex. Developers have to continuously acquire innovative requirements that provide the guideline for developing more competitive products. However, traditional methods to acquire requirements are not suitable for the App development due to the disadvantage that it cannot interact with users directly. Besides, some methods that use text and data analysis to acquire requirements automatically are hard to expand innovative products because they are often confined to the specific App or the same domain. Therefore, to attract more new users, developers try to find new portable inspiration from other domains for enriching the functions of the App. In this article, we propose a feature extraction method from the descriptions of Apps and use similarity matching to acquire cross‐domain requirements. Our experiments have verified that the Precision, the Recall, and the F‐measure are all as high as 80% of our feature extraction method. Besides, the requirements list we recommend also makes a good performance in terms of reusability with the average Reuse Rank of 59.33% and average Adjusted Functional Points of 7.49, the adaptability gets an average score of 3.3, and the average score of operability is 3.

Abstract With the rapid prevalence of smartphones and the dramatic proliferation of mobile applications, people tend to do everything at their fingertips, including some sensitive activities, such as bank transfers. This makes security become one important factor when recommending apps to users. However, most existing methods recommend apps only on the basis of the apps' functionalities. Even when some methods take security into account, they usually roughly group apps with functionalities and identify the products using extra permissions as risky, but this ignores a common phenomenon that these permissions may be used only to achieve the corresponding functionalities. In this paper, we propose an app recommendation method considering both functionalities and security. For functionalities, we summarized them from app descriptions and further evaluated their completion quality in different products by analyzing their related reviews. For security, we cluster apps with similar functionalities and quality and analyze the permissions of apps in a more comparable range. In this way, our method recommends apps with higher completion quality of functionalities and security degree to users according to their demands. We conducted experiments on apps collected from six categories of Google Play, and the results show that our method has a good recommendation effect.

Abstract Application programming interfaces (APIs) play an important role in the increasingly competitive mobile application development industry, as they can greatly improve the efficiency of app development. However, finding proper APIs is often time‐consuming for the gap between the knowledge of APIs and app features. To solve this problem, we give an approach to summarize the wisdom of developers contained in the products in app stores and establish the system of API knowledge indexed by app features for the API recommendation. First, we extract features from the app descriptions and define the feature framework. Second, we parse the APK files of apps to gain the methods in code and APIs called by them and further introduce such API knowledge into the feature framework by utilizing method names as bridges. Finally, according to features in developers' queries, we locate corresponding feature nodes in the API knowledge system and recommend related API knowledge to developers. We conduct experiments based on 38,952 apps from five categories on Google Play, and the experimental results show that our approach has a good recommendation effect for the queries on app features.

Web services provide a general basis of convenient access and operation for cloud applications. However, such services become very vulnerable when being attacked, especially in the situation where service continuity is one of the most important requirements. This issue highlights the necessity to apply reliable and formal methods to attack tolerance in Web services. In this paper, we propose a Coloured Petri Nets based method for attack tolerance by modelling and analysing basic behaviours of attack-network interaction, attack detectors and their tolerance solutions. Furthermore, complex attacks can be analysed and tolerance solutions deployed by identifying these basic attack-network interactions and composing their solutions. The validity of our method is demonstrated through a case study on attack tolerance in cloud-based medical information storage.

To support the iterative development process of Apps, the goal model is not only established to describe the requirements at the early stage but also used for identifying the updating strategy in every iteration. In this process, reviews from users provide valuable information for developers to analyse the model with users sentiments. In this study, the authors combine the goal model with reviews for supporting the evolution of Apps. First, the authors introduce the reviews into the goal model as a new factor by comparing keywords. Second, the users sentiments in reviews are mined, and two kinds of information are gained by analysing the model to help developers make decisions on which goals to be improved in next version: one kind of information is about users sentiments on the goals to evaluate whether users like them; another kind is the impact of updating one goal to others. To validate the proposed approach, they conducted experiments and a survey based on the Apps in Google Play. The results show that the proposed approach can establish relationships between goals and reviews reasonably and further provide useful information for optimising the evolution strategy of the App.

Abstract Goal model is an important model in requirements engineering, and it can describe features and their relationships for supporting the development of apps. Since an app evolves continually, the goal model also needs to be updated with new requirements to guide the whole process. As the feedback of users, reviews provide an abundant resource of user requirements for updating the goal model. In this paper, we propose an approach to help developers (a) analyze reviews to gain the information of user requirements by training a classifier and defining keyword‐based linguistic rules as well as grammar‐based rules and (b) update the goal model with the extracted information, including improving existing goals and extending the model with new goals. In addition, we design a framework to represent results so that they can be understood by developers easily. According to our experiments based on the data in Google Play, the F‐measure of classifier on reviews can reach 75.76%, and the average precision for extracting requirements‐related information from reviews is 84.04%, then we can map the information to goals with the F‐measure of 70.21%. Furthermore, the survey on 22 developers shows that the information provided by us is useful for updating the goal model.

Summary Metamorphic testing (MT) is proposed to overcome the oracle problem in software testing, and metamorphic relations (MRs) are the core of MT. There is a lack of guidelines for constructing effective MRs, and it is difficult to reuse MRs mainly because most MRs are closely related to the domain knowledge. In this article, we propose a method for constructing MRs from specifications in tabular expression format. Our method constructs MRs according to the characteristics of tabular expressions, especially the relationships between the header grids and the main grid, namely, our method is domain‐independent and the construction process is simplified. In addition, the derived MRs can be applied to specifications with the same tabular expression structure. For specifications with different tabular expression structures, MRs can still be used after slight adjustments. To evaluate the performance of our method in practice, we apply the method to five applications. The experimental results demonstrate that our method is effective for a program with the oracle problem, and that it is applicable to tabular expressions in various formats. Compared with representative testing methods, our method identifies errors that are not detected by the compared methods. Hence, our method and existing methods can complement each other. The MR proposed in this article outperforms MRs constructed based on program properties.

Summary Compared with traditional software, the domain analysis of apps is conducted not only in the early stage of software development to gain knowledge of a particular domain but also runs throughout each iteration of apps to help developers understand evolution trends of the domain for maintaining their competitiveness. In this paper, we propose an approach to analyze app descriptions combined with reviews in App stores automatically and construct a feature‐based domain state model (FDSM) in the form of state machine to support the domain analysis of apps. In FDSM, the domain knowledge up to a certain moment together is defined as a state. Initial state summarizes the high‐level knowledge by gaining topics of app descriptions, whereas each transition is generated based on the information gained within one period of time and describes the change from the current state to the next one. Furthermore, user opinions in reviews are introduced into the model to quantify the value of information for helping developers get key domain knowledge efficiently. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that FDSM can provide valuable information for supporting domain analysis, especially in the evolution process of apps.

Abstract Reviews in App stores are a massive and fast‐growing data resource for developers to understand user experiences and their needs. Studies show that users often express their sentiments on App features in reviews, and this information is important for the development and evolution of Apps. To help developers gain such information efficiently, this paper proposes a method using App descriptions, another typical data in App stores, to guide the analysis of reviews. Firstly, we extract App features from descriptions, then summarize them to gain topics of App features as high‐level information; the results are formalized as a topic‐based domain model (TBDM). Secondly, we train classifiers of reviews based on the model to establish the relationships between user sentiments and App features. Finally, a quantified method is given to analyze the model based on developer preferences for recommending and summarizing reviews. To evaluate our approach, experiments were conducted using the App descriptions and reviews collected from Google Play. The results indicate that the approach can classify reviews to their related App features effectively (average F measure is 86.13%), and provides useful information for overall analyzing App features in a domain and identifying (dis)advantages of an App.

Though the importance of ontology evolution is current recognized enough, for the sack of the complexity, most ontology evolution researches are still on the framework level or the quality analysis level. An ontology graph model derived from ontology definition metamodel (ODM) strictly, which creates ontology adjacency matrix and ontology reachability matrix, is described. Depending on matrix shift and calculation, ripple-effect of ontology evolution can be analyzed and its quantity can be ascertained. Each ripple-effect caused by ontology change operations is described. At the same time, approaches for calculating ontology element's contribution, ontology cohesions and effect degrees applied to ontology elements during ontology evolution, are provided. Ripple-effect analysis for dependency-unknown ontology evolution is discussed, and a service model for ontology evolution is also provided. All are credible foundation for management, control, usage and evaluation of ontology evolution, and are foundation for ontology evolution automation calculation in computer