The fractal property has been regarded as a fundamental property of complex networks, characterizing the self-similarity of a network. Such a property is usually numerically characterized by the fractal dimension metric, and it not only helps the understanding of the relationship between the structure and function of complex networks but also finds a wide range of applications in complex systems. The existing literature shows that class-level software networks (i.e., class dependency networks) are complex networks with the fractal property. However, the fractal property at the feature (i.e., methods and fields) level has never been investigated, although it is useful for measuring class complexity and predicting bugs in classes. Furthermore, existing studies on the fractal property of software systems were all performed on un-weighted software networks and have not been used in any practical quality assurance tasks such as bug prediction. Generally, considering the weights on edges can give us more accurate representations of the software structure and thus help us obtain more accurate results. The illustration of an approach’s practical use can promote its adoption in practice. In this article, we examine the fractal property of classes by proposing a new metric. Specifically, we build a Feature-Level Software Network (FLSN) for each class to represent the methods/fields and their couplings (including coupling frequencies) within the class and propose a new metric, Fractal Dimension for Classes (FDC) , to numerically describe the fractal property of classes using FLSNs, which captures class complexity. We evaluate FDC theoretically against Weyuker’s nine properties, and the results show that FDC adheres to eight of the nine properties. Empirical experiments performed on a set of 12 large open source Java systems show that (i) for most classes (larger than \(96\%\) ), there exists the fractal property in their FLSNs, (ii) FDC is capable of capturing additional aspects of class complexity that have not been addressed by existing complexity metrics, (iii) FDC significantly correlates with both the existing class-level complexity metrics and the number of bugs in classes, and (iv) FDC , when used together with existing class-level complexity metrics, can significantly improve bug prediction in classes in three scenarios (i.e., bug-count , bug-classification , and effort-aware ) of the cross-project context, but in the within-project context, it cannot.

Because large unstructured datasets are important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the "best" network topologies for irregular communication, unstructured graph-based interconnects can be more suitable, due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.In addition to two common stencil-based mini-applications (LULESH and Sweep3D), we analyze three popular graph workloads – clustering, pattern enumeration (triangle counting), and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, considering relevant network routing schemes. Using packet-level simulations, we report average improvements of about 4–20% and 3–26% between equivalent Jellyfish vs. Dragonfly and Jellyfish vs. Fat tree topologies across diverse input graphs and applications.

Key class identification approaches aim at identifying the most important classes to help developers, especially newcomers, start the software comprehension process. So far, many supervised and unsupervised approaches have been proposed; however, they have not considered the effort to comprehend classes. In this article, we identify the challenge of “ effort-aware key class identification ”; to partially tackle it, we propose an approach, EASE , which is implemented through a modification to existing unsupervised key class identification approaches to take into consideration the effort to comprehend classes. First, EASE chooses a set of network metrics that has a wide range of applications in the existing unsupervised approaches and also possesses good discriminatory power . Second, EASE normalizes the network metric values of classes to quantify the probability of any class to be a key class and utilizes Cognitive Complexity to estimate the effort required to comprehend classes. Third, EASE proposes a metric, RKCP , to measure the relative key-class proneness of classes and further uses it to sort classes in descending order. Finally, an effort threshold is utilized, and the top-ranked classes within the threshold are identified as the cost-effective key classes. Empirical results on a set of 18 software systems show that (i) the proposed effort-aware variants perform significantly better in almost all (≈98.33%) the cases, (ii) they are superior to most of the baseline approaches with only several exceptions, and (iii) they are scalable to large-scale software systems. Based on these findings, we suggest that (i) we should resort to effort-aware key class identification techniques in budget-limited scenarios; and (ii) when using different techniques, we should carefully choose the weighting mechanism to obtain the best performance.

For many parents, the time between their children leaving home and the ensuing arrival at school is a grey area with uncertainty. The need of being aware of the highly critical safetyness situations there- fore arises. Situation-aware computing serves as a promising technique, both theoretically and pragmatically, to bring smart monitoring to the field. It seamlessly integrates the benefits offered by machine learning techniques and cloud computing technologies with an emphasis on user centric situations. In this paper we present a real world smart monitoring application: its underlying model, its human computer interactive design and design principles, its support from the theory and practice of situation-awareness computing, as well as its implementation details and viability test. This application also serves the purpose of elaborating important attributes of safety-aware situation computing, with a strong linkage to safety critical human computer interaction. The goal of this application is to accurately recognize and identify the faces of children, under relevant environmental contexts, when they enter or exit a bus, and then promptly alert their parents. Two main components, one being a distributed bus subsystem and the other being a central cloud-based subsystem, are built into this application highlighting lightweight to effectively serve the users. The novelty of our approach lies in the fact that we deeply embrace functional programming paradigm to tackle the specification of the two subsystems at the design level with rigorous computational semantics, which in turn contributes to the quality implementation of the lower level details.

Key classes are excellent starting points for developers, especially newcomers, to comprehend an unknown software system. Though many unsupervised key class identification approaches have been proposed in the literature by representing software as class dependency networks (aka software networks) and using some network metrics (e.g., h-index, a-index, and coreness), they are never aware of the field where the nodes exist and the effect of the field on the importance of the nodes in it. According to the classic field theory in physics, every material particle is in a field through which they exert an impact on other particles in the field via non-contact interactions (e.g., electromagnetic force, gravity, and nuclear force). Similarly, every node in a software network might also exist in a field, which might affect the importance of class nodes in it. In this paper, we propose an approach, iFit, to identify key classes in object-oriented software systems. First, we represent software as a CSNWD(Weighted Directed Class-level Software Network) to capture the topological structure of software, including classes, their couplings, and the direction and strength of couplings. Second, we assume that the nodes in the CSNWDexist in a gravitation-like field and propose a new metric, CG (Cumulative Gravitation-like importance), to measure the importance of classes. CG is inspired by Newton's gravitational formula and uses the PageRank value computed by a biased-PageRank algorithm as the masses of classes. Finally, classes in the system are sorted in descending order according to their CG values, and a cutoff is utilized, that is, the top-ranked classes are recommended as key classes. The experiments were performed on a data set composed of six open-source Java systems from the literature. The results show that iFit is superior to the baseline approaches on 93.75% of the total cases, and is scalable to large-scale software systems. Besides, we find that iFit is neutral to the weighting mechanisms used to assign the weights for different coupling types in the CSNWD, that is, when applying iFit to identify key classes, we can use any one of the weighting mechanisms.

Code documentation can be helpful in many software quality assurance tasks. However, due to resource constraints (e.g., time, human resources, and budget), programmers often cannot document their work completely and timely. In the literature, two approaches (one is supervised and the other is unsupervised) have been proposed to prioritize documentation effort to ensure the most important classes to be documented first. However, both of them contain several limitations. The supervised approach overly relies on a difficult-to-obtain labeled data set and has high computation cost. The unsupervised one depends on a graph representation of the software structure, which is inaccurate since it neglects many important couplings between classes. In this paper, we propose an improved approach, named Pride, to prioritize documentation effort. First, Pride uses a weighted directed class coupling network to precisely describe classes and their couplings. Second, we propose a PageRank-like algorithm to quantify the importance of classes in the whole class coupling network. Third, we use a set of software metrics to quantify source code complexity and further propose a simple but easy-to-operate filtering rule. Fourth, we sort all the classes according to their importance in descending order and use the filtering rule to filter out unimportant classes. Finally, a threshold$k$is utilized, and the top-$k$% ranked classes are the identified important classes to be documented first. Empirical results on a set of nine software systems show that, according to the average ranking of the Friedman test, Pride is superior to the existing approaches in the whole data set.

In a very recent paper by Qu et al. (IEEE Transactions on Software Engineering, vol. 47 no. 2, pp. 348-366, Feb. 1 2021, doi:10.1109/TSE.2019.2892959), the authors propose an effective equation, top-core, to improve the performance of effort-aware bug prediction models. A distinctive feature of top-core is that it takes into account the coreness of a class in a Class Dependency Network (CDN) when calculating the relative risk of a class to be buggy. In this comment, we show that Qu et al.'s paper contains three shortcomings that may influence the performance of top-core or even have the potential to lead to erroneous results. First, we show that the CDN that they use to calculate the coreness of classes is not very accurate, neglecting many important types of dependency relations between classes such as method call relation, access relation, and instantiates relation. Second, they trained a Logistic Regression model using the scikit-learn framework to predict the probability of a specific class to be buggy. It is actually an L2 regularized Logistic Regression model, which is dependent on the scale of the features. But they neglected to normalize the features, making the obtained results erroneous. Finally, the number of execution times (viz. 10 times in the paper of Qu et al.) they used to reduce the bias caused by the randomness (viz. random split of instances and the process to handle class-imbalance problem) in the experiments is too small to ensure that the obtained results converge to stable values; but they failed to signify the precision level of their results for comparison. In this comment, we provide solutions to the problems by using i) an improved CDN (ICDN) to represent the structure of software systems, ii) the z-score method to normalize the features, and iii) an adaptive mechanism to determine the number of execution times. In the experiments, we find that Qu et al.'s approach based on the Logistic Regression model does not perform significantly better than the state-of-the-art approach Ree, which is inconsistent with the conclusion in Qu et al.'s work. We also observe that replacing CDN with ICDN does improve the performance of Qu et al.'s approach.

As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

Software comprehension is an important part of software maintenance. To understand a piece of large and complex software, the first problem to be solved is where to start the understanding process. Choosing to start the comprehension process from the important software elements has proven to be a practical way. Research on complex networks opens new opportunities for identifying important elements, and many approaches have been proposed. However, the software networks that existing approaches use neglect the multilayer nature of software systems. That is, nodes in the network can have different types of relationships at the same time, and each type of relationship forms a specific layer. Worse still, they mainly focus on identifying important classes, and little work has been done on quantifying package importance. In this paper, we propose an ElementRank approach to provide a ranked list of classes (or packages) for maintainers to start the comprehension process. The top-ranked classes (or packages) can be seen as the starting points for the software comprehension process at the class (or package) level. First, we introduce two kinds of multilayer software networks to describe the topological structure of software at the class level and package level, respectively. Second, we propose a weighted PageRank algorithm to calculate the weighted PageRank value of classes (or packages) in each layer of the corresponding multilayer software network. Then, we use AHP (Analytic Hierarchy Process) to weigh each layer in the corresponding multilayer software network, and further aggregate the weighted PageRank value to obtain the global weighted PageRank value for each class (or package). Finally, all the classes (or packages) are ranked according to their global weighted PageRank values in a descending order, and the top-ranked classes (or packages) can serve as the starting points for the software comprehension process at the class (or package) level. ElementRank is validated theoretically using the widely accepted Weyuker’s criteria. Theoretical results show that the global weighted PageRank value for classes (or packages) satisfies most of Weyuker’s properties. Furthermore, ElementRank is evaluated empirically using a set of twelve open source software systems. Through a set of experiments, we show the rank correlation between the results of ElementRank and that of the approaches in the related work, and the benefits of ElementRank are also illustrated in comparison with other approaches in the related work. Empirical results also show that ElementRank can be applied to large software systems.

Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused. To solve this problem, we build an Attribute Correlation Potential Space Generation (ACPSG) model which uses a graph convolution network and attribute correlation to generate a more discriminating potential space. Combining potential discrimination space and user-defined attribute space, we can better classify unseen classes. Our approach outperforms some existing state-of-the-art methods on several benchmark datasets, whether it is conventional ZSL or generalized ZSL.

This paper reflects on the paper titled Building Hybrid Access Control by Configuring RBAC and MAC Features which was published in Information and System Technology, 2014. The publication presents an approach for building a hybrid access control model of Role-Based Access Control and Mandatory Access Control by defining and configuring them in terms of features. The publication has been cited three times, which shows limited impact. We review the citing papers as to how the publication is cited and discuss possible reasons for the limited impact. We also discuss its position in the current state of the arts since the publication. Then, we describe an ongoing effort for a new approach to address the weaknesses of the publication with expected impact.

Changing amount of radiation dose has been a great concern by citizen near the difficult-to-return area in Japan. Rigorous assessment of public safety including inaccessible areas at a meter level is one of the keys to resolution of this concern. Observation approach in this study is the use of quadcopter UAVs (unmanned aerial vehicles) for extensively covering measurement points. However, sometimes measurement points are surrounded by different landcovers including high forest, building and uneven ground, and thus estimation accuracy depends on the geographical feature near the measurement point. This paper presents a landcover-based 3-dimensional inverse distance weighting for visualization of radiation dose in a web service system. The field test at a resident in forests was performed, and we found that the proposed method improves RMSE by 23% with the greater visibility at the roads surrounded by high forests where the average dose was 0.5 μSv/h.

Dimensional situation analytics provides a formal framework to analyze situations from Data Information Knowl-edge Wisdom (DIKW) point of view. To date, the advent of big data driven applications opens up many frontiers in artificial intelligence and computer science, and yet it also raises a series of challenges due to theirs empirical and experimental nature. In particular, to systematically and analytically understand the logical connection through which intelligence and knowledge is derived from data and information deterministically is one of the far-reaching future objectives in computer science. Starting from an earlier result on Dimensional Situation Analytics (DSA), where the initial efforts targeted at the integration of situations and DIKW ontology, this paper brings the prospect of real world applications of DSA into perspective. A good example is UAV path planning for efficient radiation detection and monitoring, which links the pervious result, i.e., the DSA formal framework, with real world experimentation and thereof further explores on the effectiveness and future work for the DSA.

Developing secure Internet of Things (IoT) applications that are free of vulnerabilities and resilient against exploit is desirable for software developers and testers. In this paper, we present IoTVerif, an automated tool that can verify SSL/TLS (Secure Socket Layer/Transport Layer Security) X.509 certificate validation of IoT messaging protocols utilized by real-world IoT client applications. IoTVerif does not require any prior knowledge about the messaging protocol, but simply correlates the observed network trace of an application with its execution context. IoTVerif helps IoT client application developers identify the SSL/TLS vulnerabilities based on certificate validation. We specifically target MQTT, a broker-based protocol that has attracted increasing popularity in the IoT application market.

Non-functional requirements (NFRs) play a crucial role in all the downstream activities of a software life-cycle process. Capturing newly emerged NFRs is key to software evolution. Recent research shows functional requirements in the form of task-level alternative features can be elicited from user behavioral and system contextual data through user goal inference. Considering the close connection between the concept of goal and desire, we posit that there is an opportunity to extract new NFRs based on users' mental states, particularly their desires. We propose to use a statistical model to infer desires with multiple-levels of abstraction based on contextual data under Situ framework. Our multi-layered desire inference method takes inference confidence into consideration, and tries to make sense of inference results with both high-and low-inference confidence. By utilizing the different abstraction levels of desires, we provide an illustrative example with three cases to elicit users' new NFRs including new high-level and low-level desires and new contributing relationships between them. Several implications of this work are also discussed. We plan to conduct experiments on human subjects to validate the proposed method as IRB has just approved our proposal.

Human factors have been increasingly recognized as one of the major driving forces of requirement changes. We believe that the requirements elicitation (RE) process should largely embrace human-centered perspectives, and this paper focuses on changing human intentions and desires over time. To support software evolution due to requirement changes, Situ framework has been proposed to model and detect human intentions by inferring their desires through monitoring environmental and human behavioral contexts prior to or after system deployment. Researchers have reported that Situ is able to infer users' desires with high accuracy using the Conditional Random Fields method. However, manual analysis is still needed for new intention identification and new requirements elicitation. This work attempts to find a computable way to identify users' new intentions with minimal help from human oracle. We discuss the feasibility of implementing the concept of DIKW (Data, Information, Knowledge, Wisdom) to bridge the gap between user behavioral & contextual data and requirements, and propose a situation-centric approach using the Multi-strategy, Task-adaptive Learning (MTL) method. A case study shows that the proposed approach is able to identify users' new intentions, and is especially effective to capture alternatives of low-level task.

User problems encountered during the use of a software product are often hard to identify even after the software product is thoroughly-tested and then released. There are inevitably unexpected situations introduced or triggered by transient use patterns, however, these unexpected situations can be hardly eliminated due to various human factors on user interaction. This study of situation-oriented real user monitoring develops an application of concept lattice to represent use patterns from user interaction and perceive potential user problems. Data structure of user problem is modeled based on user problem ontology. Once a user problem is recognized, observation data of the use patterns are the subject for further analysis. In this paper, use patterns in a file upload service are used to demonstrate how the concept lattice is used and discuss about validity of the causal factors found in the concept lattice.

While software evolution has been studied extensively in software engineering, few of these efforts have involved a systematic exploration of human epistemological attitudes, such as human desire and intention, as the driving force of software service evolution. Our work proposes a theoretical framework to monitor and reason about human intention and its changes, which in turn can be used to determine how software and services should evolve to be individualized and better serve each user. Extending the Situ framework, we explore the service satisfiability problem through sub-world coverage following Kripke semantics, which enjoys wide application in AI and other fields related to human epistemic reasoning.

Evolvability is essential for computer systems to adapt to the dynamic and changing requirements in response to instant or delayed feedback from a service environment that nowadays is becoming more and more context aware; however, current context-aware service-centric models largely lack the capability to continuously explore human intentions that often drive system evolution. To support service requirements analysis of real-world applications for services computing, this paper presents a situation-theoretic approach to human-intention-driven service evolution in context-aware service environments. In this study, we give situation a definition that is rich in semantics and useful for modeling and reasoning human intentions, whereas the definition of intention is based on the observations of situations. A novel computational framework is described that allows us to model and infer human intentions by detecting the desires of an individual as well as capturing the corresponding context values through observations. An inference process based on Hidden Markov Model makes instant definition of individualized services at runtime possible, and significantly, shortens service evolution cycle. We illustrate the possible applications of this framework through a smart home example aimed at supporting independent living of elderly people.

Organizational memories play a significant role in knowledge management, but several challenges confront their use. Artifacts of OM are many and varied. Access and use of the stored artifact are influenced by the user’s understanding of these information objects as well as their context. Theories of distributed cognition and the notion of community of practice are used to develop a model of the knowledge management system. In the present work we look at a model for managing organizational memory knowledge. Topic maps are used in the model to represent user cognition of contextualized information. A visual approach to topic maps proposed in the model also allows for access and analysis of stored memory artifacts. The design and implementation of a prototype to test the feasibility of the model is briefly examined.