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With the increased use of Artificial Intelligence (AI) in wildlife conservation, issues around whether AI-based monitoring tools in wildlife conservation comply with standards regarding AI Ethics are on the rise. This review aims to summarise current debates and identify gaps as well as suggest future research by investigating (1) current AI Ethics and AI Ethics issues in wildlife conservation, (2) Initiatives Stakeholders in AI for wildlife conservation should consider integrating AI Ethics in wildlife conservation. We find that the existing literature weakly focuses on AI Ethics and AI Ethics in wildlife conservation while at the same time ignores AI Ethics integration in AI systems for wildlife conservation. This paper formulates an ethically aligned AI system framework and discusses pre-eminent on-demand AI systems in wildlife conservation. The proposed framework uses agile software life cycle methodology to implement guidelines towards the ethical upgrade of any existing AI system or the development of any new ethically aligned AI system. The guidelines enforce, among others, the minimisation of intentional harm and bias, diversity in data collection, design compliance, auditing of all activities in the framework and ease of code inspection. This framework will inform AI developers, users, conservationists, and policymakers on what to consider when integrating AI Ethics into AI-based systems for wildlife conservation.

Fire-code inspection and compliance are among the highest priorities for fire-inspection agencies to reduce the loss of life and property that can result from fire incidents. Requirements for code compliance and inspection vary throughout towns and states within the United States, and building owners who violate these codes can be penalized via fines and mandated compliance measures. To the best of our knowledge, no previous study has investigated the strategic behavior of players in a fire-code inspection process. This paper fills the gap by presenting the game-theoretic approach to modeling building owners’ behaviors with respect to fire-code compliance and the inspection strategies of fire-inspection agencies. Both a decentralized model (sequential game in which the fire-inspection agency moves first) and a centralized model (simultaneous game controlled by one central decision maker) are developed to identify the best inspection strategies for the agency and the best compliance strategies for the building owner. This study provides prescriptive insights that can enable policymakers to improve fire-code compliance and inspection by identifying the conditions that motivate the players to participate positively in the inspection and compliance processes. Numerical sensitivity analyses of the equilibrium strategies and the expected losses of the players are provided, along with a comparison of the results between the decentralized and centralized models.

Security vulnerability prediction (SVP) can identify potential vulnerable modules in advance and then help developers to allocate most of the test resources to these modules. To evaluate the performance of different SVP methods, we should take the security audit and code inspection into account and then consider effort-aware performance measures (such as $ACC$ and $P_{\rm opt}$ ). However, to the best of our knowledge, the effectiveness of different SVP methods has not been thoroughly investigated in terms of effort-aware performance measures. In this article, we consider 48 different SVP methods, of which 36 are supervised methods and 12 are unsupervised methods. For the supervised methods, we consider 34 software-metric-based methods and two text-mining-based methods. For the software-metric-based methods, in addition to a large number of classification methods, we also consider four state-of-the-art methods (i.e., EALR, OneWay, CBS, and MULTI) proposed in recent effort-aware just-in-time defect prediction studies. For text-mining-based methods, we consider the Bag-of-Word model and the term-frequency-inverse-document-frequency model. For the unsupervised methods, all the modules are ranked in the ascendent order based on a specific metric. Since 12 software metrics are considered when measuring extracted modules, there are 12 different unsupervised methods. To the best of our knowledge, over 40 SVP methods have not been considered in previous SVP studies. In our large-scale empirical studies, we use three real open-source web applications written in PHP as benchmark. These three web applications include 3466 modules and 223 vulnerabilities in total. We evaluate these SVP methods both in the within-project SVP scenario and the cross-project SVP scenario. Empirical results show that two unsupervised methods [i.e., lines of code (LOC) and Halstead's volume (HV)] and four recently proposed state-of-the-art supervised methods (i.e., MULTI, OneWay, CBS, and EALR) can achieve better performance than the other methods in terms of effort-aware performance measures. Then, we analyze the reasons why these six methods can achieve better performance. For example, when using 20% of the entire efforts, we find that these six methods always require more modules to be inspected, especially for unsupervised methods LOC and HV. Finally, from the view of practical vulnerability localization, we find that all the unsupervised methods and the OneWay method have high false alarms before finding the first vulnerable module. This may have an impact on developers’ confidence and tolerance, and supervised methods (especially MULTI and text-mining-based methods) are preferred.

This study examines the accessibility of state occupational safety and health agency (SOSHA) websites for users with disabilities. To examine accessibility, we used a combination of AChecker-based automated analysis and manual code inspection to evaluate SOSHA consultation website accessibility. Manual code inspection looked at image ALT attribute, form label, and linked text quality, as well as the presence of skip-to-content links. Only two sites passed federal accessibility requirements based on WCAG 2.0 Level A and AA guidelines. Accessibility problems may impede a user’s quest for safety information, both for employers and employees. The results of this study also emphasize the need for researchers using automated analysis tools to supplement their research with manual site inspection.

Exploitable vulnerabilities in software have attracted tremendous attention in recent years because of their potentially high severity impact on computer security and information safety. Many vulnerability detection methods have been proposed to aid code inspection. Among these methods, there is a line of studies that apply machine learning techniques and achieve promising results. This paper reviews 22 recent studies that adopt deep learning to detect vulnerabilities, aiming to show how they utilize state-of-the-art neural techniques to capture possible vulnerable code patterns. Among reviewed studies, we identify four game changers that significantly impact the domain of deep learning-based vulnerability detection and provide detailed reviews of the insights, ideas, and concepts that the game changers have brought to this field of interest. Based on the four identified game changers, we review the remaining studies, presenting their approaches and solutions which either build on or extend the game changers, and sharing our views on the future research trends. We also highlight the challenges faced in this field and discuss potential research directions. We hope to motivate the readers to conduct further research in this developing but fast-growing field.

Code Inspection is a validation process widely used to improve the quality of software. To streamline this process and decrease the possibility of human error, improving the reliability of inspection results, it is possible to use specialized automated code inspection tools. Thus, this article proposes to analyze code inspection tools for PHP programming language, freely available on Github Marketplace. To achieve this goal, the GLPI system was chosen to be inspected, in addition, four code inspection tools were selected, out of twenty-eight available. Criteria were used for the tools selection, consistent with the sytem profile to be inspected and that do not have limitations on the inspection result. To classify the results obtained, the Common Weakness Enumeration (CWE) was used, a list of software and hardware weaknesses developed by numerous renowned companies, such as Microsoft, Apple and IBM. As a result of the inspection work, we found more than ten thousand failures divided into thirty-four different CWEs and from these we analyzed the individual feedback of each tool, as each one of them had unique advantages and disadvantages.

The software inspection process allows identifying defects during the development cycle. In the context of mobile serious games, specific inspection checklists can be employed to guarantee their quality. However, there are few tools to support the use of these checklists and the existing ones are generic and not specific for the evaluation of digital educational games for mobile devices. To assist software engineers in the evaluation of this type of games, an open-source desktop tool was developed in C#, called TIMEG (Tool for Checklist Inspection of Mobile Educational Games). TIMEG allows to: (1) embed inspection checklists; (2) point out defects based on the items in the checklists; (3) generate inspection reports; and if the game is already available in the Google Play store, (4) present information about the users’ opinions. The tool was evaluated from the point of view of inexperienced software engineers when applied for evaluating a mobile serious game; and through the answers to a questionnaire. The results indicate that the tool was perceived as useful and easy to use, in addition to identifying opportunities for improvement in the tool’s interface and usability, as well as improvements in the presentation of relevant information in the defect reports. Link para Video: Clique Aqui

Regression testing is the activity performed by developers to check whether new modifications have not introduced bugs. A crucial requirement to make regression testing effective is that test cases are deterministic. Unfortunately, this is not always the case as some tests might suffer from so-called flakiness, i.e., tests that exhibit both a passing and a failing outcome with the same code. Flaky tests are widely recognized as a serious issue, since they hide real bugs and increase software inspection costs. While previous research has focused on understanding the root causes of test flakiness and devising techniques that automatically fix them, in this paper we explore an orthogonal perspective: the relation between flaky tests and test smells, i.e., suboptimal development choices applied when developing tests. Relying on (1) an analysis of the state-of-the-art and (2) interviews with industrial developers, we first identify five flakiness-inducing test smell types, namely Resource Optimism, Indirect Testing, Test Run War, Fire and Forget, and Conditional Test Logic, and automate their detection. Then, we perform a large-scale empirical study on 19,532 JUnit test methods of 18 software systems, discovering that the five considered test smells causally co-occur with flaky tests in 75% of the cases. Furthermore, we evaluate the effect of refactoring, showing that it is not only able to remove design flaws, but also fixes all 75% flaky tests causally co-occurring with test smells.

This research is aimed at introducing an enhanced inspection model known as Improved Software Inspection Model (ISIM) by making some enhancements to overcome or reduce the weaknesses identified in Formal model. ISIM includes a method of selecting suitable inspectors based on their expertise and work experience; provide a database that stores information on potentials; has a database that stores the possible causes of each of the defects stored in the defect database; and incorporates a few enhancements in the inspection process. To facilitate the selection of suitable inspectors and the inspection process, an inspection tool was developed using PHP with MySQL as database management system. To determine whether the use of ISIM can improve the inspection process, the quality of the process was evaluated using paired T-test which compares the differences between Formal and ISIM models, in terms of the number of defects detected, inspection time, and the productivity of the inspectors. Two case studies were conducted in two companies and inspections were carried out on two software development projects to collect the data needed for the statistical test. The statistical test results show that at ii = 0.05, the mean values of the number of defects are detected; the inspection time; and the productivity of the inspectors when using Formal and ISIM are 19.78 and 28.13; 220.97 minutes and 213.69 minutes; and 0.0891 and 0.1328, respectively. This shows that there are significant improvements in the number of defects detected (i.e. difference = 8.35; 42.21%), and productivity of the inspectors (i.e. difference = 0.0437; 49.05%). However, there is only a slight improvement in the inspection time. It can be concluded that ISIM together with its tool improve the quality of inspection process in the two case studies.

Software bug prediction techniques can be very helpful in testing and code inspection. Over the past decade, network measures have been successfully used in bug prediction. Following the same intuition, recently, researchers started using network embedding techniques in bug prediction. However, existing studies only evaluated the Skip-gram and CBOW models with random walk. Considering network embedding is a fast-developing research direction, it is important to evaluate other network embedding techniques’ performances in bug prediction. Moreover, existing studies have not investigated the application and performance of network embedding in effort-aware bug prediction, which is thought to be a more realistic scenario that evaluates the cost effectiveness of bug prediction models. In this paper, we conduct an extensive empirical study to evaluate network embedding algorithms in bug prediction by utilizing and extending node2defect, a newly proposed bug prediction model that combines the embedded vectors with traditional software engineering metrics through concatenation. Experiments are conducted based on seven network embedding algorithms, two effort-aware models, and 13 open-source Java systems. Experimental results show that node2defect outperforms traditional metrics by + 14.64% in terms of MCC score, and by + 7.51% to + 16.57% in effort-aware bug prediction. More interestingly, when combined with CBS + , the embedded vectors alone can achieve the best performance. Among different network embedding algorithms, the newly proposed algorithm ProNE has the best performance.

In software development, stakeholders usually collaborate asynchronously by sharing artifacts. Traceability system links artifacts and henceforth offers assist collaboration among system requirements. Various stakeholders show their interest towards various kinds of traceability and system requirements. With analysis towards various investigations, system requirements have to be fulfilled by prioritizing tasks. However, it is extremely complex to maintain or fulfil system requirements. But, none of the background investigations have been analyzed with heuristic based requirement traversing. This work investigates traversing of users’ functional requirements with Meta-heuristic approach termed as adaptive optimization and with optimal priority scheduling. With the assistance of traceability concept, functional requirements, design, test artifacts, defects, code inspection and system construction are considered and come under maintenance phase. It is also essential to analyze huge industrial firms to depict link and traceability types. Traceability deals with relationship among requirements, their source and system design. Bi-directional traceability, which deals with various associated requirements enables user to find origin of requirements and track every changes that are made to the requirements. The foremost goal of this work is to establish traceability links to provide support for decision making, to resolve inconsistency problem and to demonstrate quality requirements with improved system lifetime. The anticipated model shows better trade-off in contrast to prevailing models.

GIS is prone to various defects and faults after putting into operation, and the failure rate is high. At present, the inspection methods of GIS in operation include traditional inspection mode, two-dimensional code inspection mode, radio frequency identification technology mode and robot inspection mode. However, the current inspection robot has the disadvantages of weak adaptability to complex environment, insufficient inspection scope, low efficiency and low intelligence. Therefore, this paper designs a GIS monitoring system based on the rail robot. By installing all kinds of sensors on the rail robot, with the help of binocular vision and mechanical arm, the omni-directional live detection of GIS in transit can be realized.

Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting. In this paper, we investigate the effectiveness of state-of-the-art effort-aware JIT defect identification approaches in an industrial setting. To that end, we conduct a case study on 14 Alibaba projects with 196,790 changes. In our case study, we investigate three aspects: (1) The effectiveness of state-of-the-art supervised (i.e., CBS+,OneWay, EALR) and unsupervised (i.e., LT and Code Churn) effortaware JIT defect identification approaches on Alibaba projects, (2) the importance of the features used in the effort-aware JIT defect identification approach, and (3) the association between projectspecific factors and the likelihood of a defective change. Moreover, we develop a tool based on the best performing approach and investigate the tool's effectiveness in a real-life setting at Alibaba.

Software Inspection is a review technique that aims to improve the quality of software artifacts through defect detection, thus reducing the cost of rework. Being a human-driven process, it depends directly on the professional's individual expertise level in the field. However, it is challenging to teach software inspection in the classroom without proper support, leading students to lose motivation and hampering their learning. To circumvent that, the InspectorX serious game was proposed as a means to support Software Inspection Education and Training. Since then, students provided feedback on what could make it more interesting. In this sense, this paper introduces InspectorX 2.0, an evolution that migrate old issues and present novel features. A preliminary assessment performed with students helped us understand the pros and cons of this version.