International Journal of Software Engineering and Knowledge Engineering

Today’s society is increasingly dependent on the use of mobile or smart devices (e.g. smartphones, tablets, hybrid devices, smart-TVs, smart-watches, among others), which have changed over these last 10 years the way people perform their daily tasks. In parallel, Internet of Things (IoT) systems have played a prominent position in this scenario, since they enable to exchange information among different types of devices and services (e.g. physical devices, vehicles, home appliances, sensors, among others). This scenario has boosted the demand for development of high-quality Mobile Applications (MobApps), which can be pre-installed on such devices during manufacturing platforms, or delivered as applications by the mobile stores or third parties. According to [1–3], mobile devices have some physical limitations (e.g. processing and storage) compared to personal computers. Thus, service-oriented MobApps have been a feasible alternative to overcome such limitations, improving the efficiency of the development life cycle of these applications with adoption of third-party components (e.g. software components, web services, and other mobile applications). In another perspective, it is also been noted a change in the user behavior of MobApps, which demand applications capable of operating in adverse conditions, maintaining their integrity of execution. Considering the relevance of such applications, this paper reports the extension of a framework to support the development of Self-adaptive Services-oriented MobApps (Self-MobApps), which enables modification of services at runtime [4] by means of a deployment dynamic approach. To show the feasibility of our framework, a case study for a smart restaurant was conducted in a mobile environment. The results of this study enable us to create a positive perspective on the contribution of our framework to the research communities involved.

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with the state-of-the-art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover high-quality latent topics from noisy short texts.

The timing requirements of real-time systems can be guaranteed by well-designed scheduling policies. The analysis of such scheduling uses an abstract task model of the system to diagnose the practicability of timing requirements. The task models have evolved from periodic models to more sophisticated graph-based ones, among which digraph real-time (DRT) task model is the most applicable because of its good expressiveness and analysis efficiency. However, the DRT model cannot support the commonly used precedence constraints within or between tasks. In this paper, we propose a new task model that extends the DRT model to support precedence constraints. Based on our model, we present two methods of uniprocessor schedulability analysis for static priority scheduling policy and earliest deadline first (EDF) scheduling policy. We also introduce an optimization technique to improve the efficiency of model analysis. Our experiments show that, despite a high computational complexity of the problem, our approach scales very well for large sets of tasks with precedence constraints.

Because of the widespread usage of Android devices, the Android ecosystem has the highest numbers of users, developers, and app downloads. Researchers find that many Android apps are not sufficiently tested, which may lead to crashes, incorrect behaviors, and security vulnerabilities. Mutation testing is a syntax-based software testing technique that is very effective at designing high-quality tests and evaluating pre-existing tests. Our prior research designed and implemented Android mutation testing technique, and then used experiments to assess its strength. However, the high computational cost of Android mutation testing possibly limits its industrial application. This paper presents an experimental evaluation that investigates redundant mutation operators in Android mutation analysis. While maintaining the test quality, our goal is to reduce the cost by excluding redundant mutation operators or improving their design and implementation. In our evaluation, we first generate mutants and design mutation-adequate tests for each mutation operator. Then, we compute redundancy scores for each pair of mutation operators. Our evaluation results indicate that three operators (AODU, AOIU, and LOI) are redundant in Android mutation analysis. Other three operators (FOB, TVD, and ORL) are very hard to kill. One operator (MDL) needs improvement in its design to eliminate trivial mutants. We also identity subsumption relationships among operators (BWS subsumes BWD, ODL subsumes CDL, COD, and VDL).

IEEE 802.11i is the IEEE standard that provides enhanced MAC security and has been widely used in wireless networks and Internet of Things. It improves IEEE 802.11 (1999) by providing a Robust Security Network (RSN) with two new protocols: the Four-Way Handshake and the Group Key Handshake. These protocols utilize the authentication services and port access control described in IEEE 802.1X to establish and change the appropriate cryptographic keys. In this paper, we carry out a formal modeling and verification approach based on timed automata for IEEE 802.11i protocol, using the UPPAAL model checker, to check correctness of the changes in IEEE 802.11i protocol and provide better security.

An intruder of a company’s network may use stolen login credentials to silently collect sensitive data. Such malicious user behavior is difficult to detect as long as it does not trigger access violation or data leak alert. In this paper, we propose to use an ensemble of three unsupervised anomaly detection algorithms, namely OCSVM, RNN and Isolation Forest, to detect abnormal user behavior patterns. Besides, an User Behavior Analytics (UBA) Platform is proposed to collect logs, extract features and conduct experiments. The experiment results indicate that our algorithm outperforms each individual algorithm with recall of 96.55% and precision of 91.24% on average, while both OCSVM and RNN suffer from anomalies in the training set, and $iForest$ produces more false positives and false negatives in prediction.

Software-Defined Networking (SDN) is an emerging architecture of computer networking. OpenFlow is considered as the first and currently most popular standard southbound interface of SDN. It is a communication protocol which enables the SDN controller to directly interact with the forwarding plane, which makes the network more flexible and programmable. The promising and widespread use makes the reliability of OpenFlow important. The OpenFlow bundle mechanism is a new mechanism proposed by OpenFlow protocol to guarantee the completeness and consistency of the messages transmitted between SDN devices like switches and controllers. In this paper, we use Communication Sequential Processes (CSP) to formally model the OpenFlow bundle mechanism. By adopting the models into the model checker Process Analysis Toolkit (PAT), we verify the relevant properties of the mechanism, including deadlock freeness, parallelism, atomicity, order property and schedulability. Our formalization and verification show that the mechanism can satisfy these properties, from which we can conclude that the mechanism offers a better way to guarantee the completeness and consistency.

One of the more complex topics to teach to Software Engineering students is the conceptual modeling design, which has several concepts that students must learn in order to specify the structural, behavioral and interaction views of software systems. Learning the design of class diagrams is of paramount importance since these diagrams are used to guide concrete development tasks such as programming and testing, and, consequently, to avoid defective software products. Applying novel teaching/learning techniques in this topic may help students to reduce the defects that are committed at the moment of designing a class diagram. One interesting technique is the use of serious games, since they provide learning environments free of risks and pressure for students, allowing the students to know the topics that they must learn in a funny way. Serious games have been widely used in programming courses. We aim to investigate the feasibility to replicate this experience for conceptual modeling of class diagrams at Software Engineering courses. In this paper, we present a role-playing game especially focused on the class diagram, which is called Classutopia. This serious game provides modeling challenges, comprehension and correction of diagrams with different complexity levels for learning conceptual modeling design.

Fuzzing is an important method for binary vulnerability mining. It can analyze binary programs without their source codes, which is not easy to do by other technologies. But due to the blindness of input generation, binary fuzzing often falls into traps for a long time when the new mutated inputs cannot generate unexplored paths. In this paper, we propose an efficient and flexible fuzzing framework named Tinker. It defines the growth rate of path coverage to measure the current state of fuzzing. If the fuzzing falls into low-speed or blocked states, a symbolic analysis procedure is invoked to generate a new input which can help the fuzzing jump out of the trap. In the symbolic analysis procedure, we employ dynamic execution to track the traversed nodes. The untraversed branches are then identified according to the recorded data of American Fuzzy Lop (AFL) [M. Zalewski, American Fuzzy Lop (2014), http://lcamtuf.coredump.cx/afl/]. At last, we employ control flow graph (CFG) to construct complete paths to these branches and a new input is generated using symbolic execution. Moreover, to expedite the detection of vulnerabilities, we generate inputs which trigger more high-risk system calls first, such that the possibility of finding vulnerabilities can be improved. Tinker has been implemented and the experiments on DARPA CGC benchmark show that Tinker is more efficient in vulnerability mining than state-of-the-art binary vulnerability mining tools.

In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion.

Propositionalization has been proven to be a very effective solution for multi-relational data mining problems. The approaches usually follow a two-step principle: transforming the relational data into a single, flat table and applying a propositional learning algorithm. During the transformation, the target table gets expanded by adding many new features summarizing the information of the non-target tables. Based on the used feature construction strategy, this leads to a table of very high dimensionality with a lot of irrelevant and/or redundant features that can negatively affect the predictive performance. In this paper, we propose a modification of the traditional two-step framework to overcome such problems. The proposed approach evaluates the features during the construction phase and reports only a subset of highly predictive features to the propositional learner. We present an implementation of this approach using a genetic algorithm to search for an optimal feature subset. Our experiments on a number of benchmark datasets suggest that the modified framework can help propositionalization methods to significantly improve their predictive performance.

Advancements in mobile, cloud computing and other techniques have made the world even smaller and connected like never before. It has become a challenge and an opportunity for cities to leverage these growing technologies to solve real city administration problems. Cities are in the transformation to become state-of-the-art smart cities using these technologies. This paper is about the automation of street cleanliness assessment in near real-time. It answers the question of how can we assess the status of streets in a more efficient and effective way. To address the problem, this paper proposes a multi-level assessment system on how the cleanliness status of streets is collected using mobile stations. They are connected via city network, analyzed in the cloud and presented to city administrators online or on mobile. The real case studies show the usability and feasibility of our system. This also gives opportunities for city residents to participate and contribute to making the city a better place.

Despite the many integration tools proposed for mapping between OWL ontologies and the object-oriented paradigm, developers are still reluctant to incorporate ontologies into their code repositories. In this paper we survey existing approaches for OWL-to-OOP mapping trying to identify reasons for this shy adoption of ontologies among conventional software developers. We present a classification of the surveyed approaches and tools based on their technical characteristics and their resulting artifacts. We discuss further potential reasons beyond what have been addressed in the literature before finally providing our own reflection and outlook.

Software organizations need to increase their productivity to stay competitive. Although there is a lot of research on productivity in software development, software organizations still do not know what are the most significant productivity factors in which they should invest. This paper presents a Tertiary Literature Review (TLR) that aims to identify and analyze Systematic Literature Reviews (SLR) on the influence factors of software productivity reported in the scientific literature. We extracted and classified the influence factors into organizational factors (organizational-dependent factors) and human factors (people-dependent factors). The relevance of the factors was extracted according to the amount of references found in the secondary studies. Using this information, software organizations can improve the productivity of their projects by evaluating the influence factors that best fit their context.

One of the main issues of an agile software project is how to accurately estimate development effort. In 2014, a Systematic Literature Review (SLR) regarding this subject was published. The authors concluded that there were several gaps in the literature, such as the low level of accuracy of the techniques and little consensus on appropriate cost drivers. The goal of our work is to provide an updated review of the state of the art based on this reference SLR work. We applied a Forward Snowballing approach, in which our seed set included the former SLR and its selected papers. We identified a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD). We also identified that there is a gap in terms of agreement on suitable cost drivers. Thus, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation. This updated review of the state of the art resulted in 24 new relevant papers selected.