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Code clones are comparable code snippets that are introduced into software by developers in order to increase software development productivity. A change to code clone may result in a consistent-defect if the developers forget to verify the consistency of the code after the change. To reduce such change-related maintenance costs, researchers have proposed a number of methods for predicting clone consistency in advance. Unfortunately, the effectiveness of these cross-project models is unsatisfactory, and performing such predictions with insufficient data remains a challenge. Meanwhile, cross-project defect prediction via transfer learning method is prevalent in the software engineering community. Consequently, we first construct an empirical study to explore whether transfer-learning techniques could well be utilized for clone cross-project consistent-defect prediction in the initial stages of software development. In this paper, we employ transfer-learning techniques to predict clone consistency at both the time of clone creating and clone changing in order to avoid clone consistent-defects and maintenance. We conduct an experiment on open-source projects to evaluate the effectiveness of various transfer-learning methods. Our investigation demonstrates that transfer-learning techniques have a beneficial impact on predicting cross-project clone consistent-defect, and that the size of the dataset also has a positive effect on prediction. In order to promote software safety and security, we recommend that developers leverage transfer-learning to enhance the capability for clone cross-project consistent-defect prediction early in the software development phase. • We propose a clone cross-project consistent-defect prediction approach for both clone-creating and changing times. • We first investigate the effectiveness of transfer-learning methods for clone cross-project consistent-defect prediction. • We conduct experiments on eight open-source projects to address our research problem. [ABSTRACT FROM AUTHOR]

• We proposed a random approximate reduct-based ensemble algorithm. • The technique of random approximate reduct was used to perturb the attribute space. • We applied the proposed algorithm to the problem of software defect prediction (SDP). • We designed a hybrid mechanism to tackle the class imbalance issue in SDP. • Experimental results show that our algorithm is effective for SDP. Software defect prediction (SDP) is an important research topic in software engineering. It can optimize the allocation of testing resources by indicating the defect-prone software modules. In recent years, ensemble learning has been frequently used in SDP. Ensemble learning can effectively improve the generalization ability of individual learners, but how to increase the diversity of individual learners is still an open issue. In this paper, we propose a random approximate reduct-based ensemble algorithm called ERAR. To increase the diversity of learners generated by ERAR, a novel technique, called random approximate reduct (RAR), is proposed to perturb the attribute space, and the resampling technique is used to perturb the instance space. RAR is derived from the random subspace method and the granular decision entropy-based reduct in rough set theory. We then investigate the application of ERAR in SDP. In order to alleviate the class imbalance issue in SDP, we further design a hybrid mechanism by combining ERAR with the Synthetic Minority Over-sampling Technique (SMOTE). Experimental results on 20 benchmark data sets show that the hybrid mechanism can provide competitive solutions for SDP. [ABSTRACT FROM AUTHOR]

An ontology is a formal, explicit specification of a shared conceptualization. Ontologies are used in many fields, such as software engineering, information extraction, semantic search, knowledge management, recommender systems, etcetera. Since manual ontology building is a very costly, time-consuming, and error-prone task, automating the process of ontology building, or in other words, learning ontology from existing resources, is a good option. Nowadays, a large amount of data on the web is stored in relational databases, but databases cannot be used directly in the semantic web. Hence, in this paper, we have proposed a new approach to automatically creating an OWL ontology from a relational database. We have defined a set of rules to analyze all database components and convert them to corresponding ontology components. The core contribution of our work is the set of rules which can analyze and extract ontology elements from stored procedures, user-defined functions, views, multiple inheritance, the specific representation of single inheritance, common attributes, and the constraints on tables and their columns. The proposed approach has been compared with existing approaches using three frameworks. [ABSTRACT FROM AUTHOR]

Security vulnerabilities are among the major concerns of modern software engineering. Successful results of machine learning techniques in various challenging applications have led to an emerging field of research to investigate the effectiveness of machine learning, and more recently, deep learning techniques, for the problem of software vulnerability analysis and discovery. In this paper, we explore the utilization of Graph Neural Networks as the latest trend and progress in the field of artificial neural networks. To this end, we propose an original neural vulnerability analysis approach, using customized intermediate graph representations of programs to train graph neural network models. Experimental results on a public suite of vulnerable programs show that the proposed approach is effective at the task of software vulnerability analysis. Additional empirical experiments answer complementary research questions about the proposed approach. In particular, we present experimental results for the challenging task of cross-project vulnerability analysis, with interesting insights on the capabilities of our novel approach. Furthermore, a software utility that was developed in the course of this study is also published as a further contribution to the research community. [ABSTRACT FROM AUTHOR]

Accurate estimation of energy savings is crucial for the effective implementation of energy conservation measures (ECMs). Simultaneously, the integration of Artificial Intelligence (AI) has revolutionized software engineering by imbuing software with intelligent capabilities and autonomy. In this paper, we propose an ensemble model for precisely estimating baseline energy consumption within the realm of AI-empowered autonomous software. The ensemble model combines predictions from three tree-based Machine Learning models, namely Random Forest, XGBoost, and LightGBM. Notably, our model emphasizes the provision of explainability, granting transparency and insights into the key factors influencing baseline energy consumption. To validate its effectiveness, we conduct experimental evaluations on a diverse cluster of real-world buildings in Latvia. The results demonstrate the superiority of our proposed ensemble model over individual models and even a deep learning network tailored for energy consumption estimation. These findings underscore the efficacy of AI-empowered models in the energy sector, offering a robust and interpretable solution for estimating energy savings. [ABSTRACT FROM AUTHOR]

• We evaluate how a proposed set of privacy guidelines can be used to effectively improve the IoT application designs. In support of this, we integrate the guidelines with a method for applying them to proposed a PbD framework for IoT applications. • Our method is uniquely designed to address IoT challenges such as their heterogeneity and distributed nature. This is a significant difference from existing PbD frameworks, which focus on more general, high-level principles and design strategies (e.g., [5,3]). • We gain insights into how our framework could help software engineers to better design privacy aware IoT applications by identifying and applying 45 privacy protecting features into their designs. • We also explore strengths and weaknesses of our approach as well as challenges in manual application design processes in general. We provide insights on how to address these weaknesses. Internet of Things (IoT) applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. However, thus far, privacy concerns have not been explicitly considered in software engineering processes when designing IoT applications. With the advent of behaviour driven security mechanisms, failing to address privacy concerns in the design of IoT applications can also have security implications. In this paper, we explore how a Privacy-by-Design (PbD) framework, formulated as a set of guidelines, can help software engineers integrate data privacy considerations into the design of IoT applications. We studied the utility of this PbD framework by studying how software engineers use it to design IoT applications. We also explore the challenges in using the set of guidelines to influence the IoT applications design process. In addition to highlighting the benefits of having a PbD framework to make privacy features explicit during the design of IoT applications, our studies also surfaced a number of challenges associated with the approach. A key finding of our research is that the PbD framework significantly increases both novice and expert software engineers' ability to design privacy into IoT applications. [ABSTRACT FROM AUTHOR]

Formal Concept Analysis, as a mathematical tool, has been applied successively in diverse fields such as data mining, conceptual modeling, social networks, software engineering, and the semantic web, to cite but a few. One of the utter shortcoming of Formal Concept Analysis, however, is the large number of formal concepts that are extracted from even reasonably sized formal contexts. This overwhelming number was a key hindrance for a larger utilization of the technique (FCA). To overcome this shortcoming, only extracting a minimal coverage of formal concepts could be a remedy. Even though this task has been shown to be NP-hard, it attracted the attention of many researchers. In this paper, we introduce a new gain function based approach, called QualityCover , for the extraction of a pertinent coverage of a formal context. This algorithm operates akin to a greedy approach and relies on the assessment of a measure of correlation for the selection of the formal concepts to be retained in the final coverage. Extensive experiments show that QualityCover obtains very encouraging results versus those obtained by pioneering approaches in the literature. [ABSTRACT FROM AUTHOR]

Software systems tend to become larger and more complicated as the functional requirements increase gradually. Well-modularized software systems are widely believed to be understood, managed and evolved easily. Software module clustering problems (SMCPs) are significant and challenging problems in software engineering whose primary aim is to obtain perfect structures of software systems according to predefined rules. To solve SMCPs, it is important to design a measure which can appropriately evaluate the quality of the structures obtained. So far, the modularization quality (the basic MQ ) is widely used as the conventional measure. However, this measure does not consider global modules and edge directions between two modules. Therefore, in this paper, we design a new modularization quality measure based on the similarity, labeled as MS , to automatically guide optimization algorithms to find a good partition of software systems which considers both global modules and edge directions. To systematically validate the performance of MS , three optimization algorithms, namely hill-climbing algorithm (HC), genetic algorithm (GA), and multi-agent evolutionary algorithm (MAEA), are adopted to optimize MS on 17 real-world software systems with varying sizes. A thorough comparison shows that MS outperforms the basic MQ in guiding the optimization algorithms to find more meaningful module clusters for software systems. [ABSTRACT FROM AUTHOR]

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-objective optimisation problem. This paper further develops the multi-objective agenda by adapting a decomposition-based multi-objective evolutionary algorithm (MOEA/D). Experiments evaluated four approaches: a classic greedy algorithm; non-dominated sorting genetic algorithm II (NSGA-II); MOEA/D with a fixed value for a parameter c ; and MOEA/D in which tuning was used to choose the value of c . These used six programs from the SIR repository and one larger program, VoidAuth. In all of the experiments MOEA/D with tuning was the most effective technique. The relative performance of the other techniques varied, although MOEA/D with fixed c outperformed NSGA-II on the larger programs (Space and VoidAuth). [ABSTRACT FROM AUTHOR]

Online social networks have become so pervasive in people’s lives that they can play a crucial role in design and development processes of applications. At moment, a gap exists w.r.t. standard networking programming to support social-network-based programming in large, according to software engineering principles of genericity and polymorphism. This drawback is made evident when applications should be built on top of multiple social networks and the user-centered vision should be kept. Indeed, heterogeneity of social networks does not allow us to produce software with suitable abstraction. In this paper, we cover the above gap by defining and implementing a model aimed at generalizing concepts, actions and relationships of existing social networks. The effectiveness of our approach is shown by two case studies. [ABSTRACT FROM AUTHOR]

Most of the research effort in the area of software analysis is focused on the perspective of the developer (as in “software developing company”) and the ways how the software development process could be improved. However, that is not the only type of software assessment common in the industry. There are also assessments that are commissioned by other parties, such as the primary recipients of the software solutions or courts dealing with legal cases that are related to software products or services. This work presents one such case-study that was performed for a public administration in Italy. The paper describes the assessment itself and also points out the need for more focused research by providing a comparison between developer-oriented and customer-oriented assessment types. [ABSTRACT FROM AUTHOR]

Software architectures constitute important analysis artefacts in software projects, as they reflect the main functional blocks of the software. They provide high-level analysis artefacts that are useful when architects need to analyse the structure of working systems. Normally, they do this process manually, supported by their prior experiences. Even so, the task can be very tedious when the actual design is unclear due to continuous uncontrolled modifications. Since the recent appearance of search based software engineering, multiple tasks in the area of software engineering have been formulated as complex search and optimisation problems, where evolutionary computation has found a new area of application. This paper explores the design of an evolutionary algorithm (EA) for the discovery of the underlying architecture of software systems. Important efforts have been directed towards the creation of a generic and human-oriented process. Hence, the selection of a comprehensible encoding, a fitness function inspired by accurate software design metrics, and a genetic operator simulating architectural transformations all represent important characteristics of the proposed approach. Finally, a complete parameter study and experimentation have been performed using real software systems, looking for a generic evolutionary approach to help software engineers towards their decision making process. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. A proper instantiation of a data structure can greatly impact the performance and the efficiency of the software system. In this paper we are focusing on the problem of customizing data structures instances during the execution of software systems using a supervised learning approach. In order to customize a data structure instance according to the software system’s current execution context, a neural network will be used. We experimentally evaluated our technique on a case study, emphasizing the advantages of the proposed approach. The obtained experimental results highlight the potential of our proposal in using a supervised learning based approach for dynamically configuring data structures instances. [Copyright &y& Elsevier]