Nowadays, software is ubiquitous and present in almost everything we buy and use. Artificial intelligence (AI) is becoming prevalent in software products. The use of AI entices consumer inquisitiveness, promising software products that can make our lives easier, productive, and in some mission-critical applications safer. Similar reasoning can be applied to systems exploring Internet of Things, cloud services, and mobile technologies. However, there is a trust deficit when it comes to accepting AI as well as the other above-mentioned features, as a reliable technology platform. This paper argues that the more critical the domain is, the less consumers seem to trust software to make decisions on their behalf or even to be used. Aspects such as safety, privacy, and ethics challenges the perception of trustworthy computing. In the past two decades, several works have suggested that Corporate Social Responsibility (CSR) may play an essential role in creating a trust paradigm between customers and businesses promoting loyalty, customer retention and thus enhancing customer trust and increasing corporate profit. We believe that the software industry will need soon rather than later to encourage trust in their embedded software. A promising approach lies in adapting principles associated with CSR to guide the software development processes. Such an approach could help to achieve two goals: Deliver trustworthy software and, if desired, deliver socially responsible software. We believe that Non-Functional Requirements (NFR) will play a crucial role in this endeavor. This paper highlights a first approach to establishing a basic set of NFRs that should always be carefully considered when developing software, as to aim socially responsible software.

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Non-Functional Requirements Orienting the Development of Socially Responsible Software

The application of Software Fault Prediction (SFP) in the software development life cycle to predict the faulty class at the early stage has piqued the interest of various scholars. In the SFP domain, during research analysis, it got realized that there has been very little work instigated on addressing both class imbalance and feature redundancy problems jointly to enhance the performance and prediction accuracy of SFP models. It has been perceived in the literature survey the study of droughts with the comprehensive comparative analysis of different sampling and feature selection strategies together. This research builds an extensive assessment of distinct combinations of different feature selection and sampling approaches, to effectively overcome the problems of class overlap, class imbalance, and feature redundancy. The objective is to determine the best combination that will produce results with a higher degree of accuracy and an effective SFP model. Considering the above erudition, the study has applied 8 different sampling techniques along with 10 feature selection algorithms against 56 open-source projects. The comparative analysis is performed against 5346 variants of input datasets by applying 8 different classifiers to predict the faulty class. In addition, the research paper presents an intensive assessment and performance of these techniques individually against all the input projects. We have considered accuracy and Area Under the ROC (receiver operating characteristic curve) Curve (AUC) performance metrics to compare the performance of different models developed using the classification algorithm. For each project in the proposed work, we evaluated a total of 792 combinations that were produced using 10 feature selection methods, 1 all metrics dataset, 8 sampling methods, 1 original, unsampled dataset, and 8 classifiers. The empirical result indicates that, against 21 projects out of 54 projects, Synthetic Minority Over Sampling Technique Edited (SMOTEE) with correlation-based feature selection (FS2) combination outperformed with the highest AUC value which is 38.89 % of projects. Additionally, according to experimental results, the highest AUC values were attained by 24.07 % of projects using the SMOTEE, FS2, and RF combination. The results of the statical analysis test reveal that 93.42 % of the combinational pairs of different sampling and feature selection approaches demonstrated a significant variance in the performance of the distinct combinations of sampling and feature selection techniques. The empirical result indicates the performance of the SFP Model is adversely impacted by class imbalance and irrelevance. The outcome indicates for more than 75% of projects, the performance of trained models improved with an AUC value between a range of 0.805 to 0.99 post-application of sampling and feature selection strategies, in comparison without the use of feature selection and sampling techniques. 

Empirical evaluation of the performance of data sampling and feature selection techniques for software fault prediction

Software quality is defined as the degree to which a software component or system meets specified requirements and specifications. Assessing software quality in the early stages of design and development is crucial as it helps reduce effort, time and money. However, the task is difficult since most software quality characteristics (such as maintainability, reliability and reusability) cannot be directly and objectively measured before the software product is deployed and used for a certain period of time. Nonetheless, these software quality characteristics can be predicted from other measurable software quality attributes such as complexity and inheritance. Many metrics have been proposed for this purpose. In this context, we speak of estimating software quality characteristics from measurable attributes. For this purpose, software quality estimation models have been widely used. These take different forms: statistical models, rule-based models and decision trees. However, data used to build such models is scarce in the domain of software quality. As a result, the accuracy of the built estimation models deteriorates when they are used to predict the quality of new software components. In this paper, we propose a search-based software engineering approach to improve the prediction accuracy of software quality estimation models by adapting them to new unseen software products. The method has been implemented and favorable result comparisons are reported in this work.

A Hybrid Heuristic Approach To Optimize Rule-Based Software Quality Estimation Models.

Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Software testing techniques are crucial for detecting faults in software and reducing the risk of using it. As such, it is important that we have a good understanding of how to evaluate these techniques for their efficiency, scalability, applicability, and effectiveness at finding faults. This article enhances our understanding of software testing technique evaluations by providing an overview of the state of the art in research and structuring the field to assist researchers in locating types of evaluations they are interested in. To do so a systematic mapping study is performed. Three hundred and sixty-five primary studies are systematically collected from the field and each mapped into categories based on numerous classification schemes. This reveals the distribution of research by each category and identifies where there are research gaps. It also results in a mapping from each combination of categories to actual papers belonging to them; allowing researchers to very quickly locate all of the testing technique evaluation research with properties they are interested in. Further classifications are performed on case study and experiment evaluations in order to assess the relative quality of these evaluations. The distribution of research by various category combinations is presented along with a large table mapping each category combination to the papers belonging to them. We find a majority of evaluations are empirical evaluations in the form of case studies and experiments, most of them are of low quality based on proper methodology guidelines, and relatively few papers in the field discuss how testing techniques should be evaluated.

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Evaluating software testing techniques: A systematic mapping study

Predicting software faults before software testing activities can help rational distribution of time and resources. Software metrics are used for software fault prediction due to their close relationship with software faults. Thanks to the non-linear fitting ability, Neural networks are increasingly used in the prediction model. We first filter metric set of the embedded software by statistical methods to reduce the dimensions of model input. Then we build a back propagation neural network with simple structure but good performance and apply it to two practical embedded software projects. The verification results show that the model has good ability to predict software faults.

Embedded Software Fault Prediction Based on Back Propagation Neural Network

Software quality encompasses many characteristics. Within limited resources, how to select the most important characteristics for quality assessment remains an important question. This paper designed a survey to prioritize the goals and software quality characteristics in safety-critical domains. The data were collected from 34 practitioners from 18 organizations in the Chinese safety-critical industries. The results provide guidance on better allocating resources to software quality assessment in safety-critical domains.

What Software Quality Characteristics Most Concern Safety-Critical Domains?

fault injection is an effective technique in software testing. By introducing faults to software under test, fault injection can improve the coverage of a test, as the same time, the fault injected in software contributes significantly to find true fault related to fault injected. In this paper we propose a software testing method based on fault injection. In this method, we first use neural network to calculate the value of fitness function that describe the proximity of two paths, and then use simulated annealing algorithm to generate test data. In this method we use the value of fitness function as the criteria in simulated annealing algorithm. Experiment shows that this method can improve the efficiency of generating test data obviously.

Use Neural Network to Improve Fault Injection Testing

Cyber-Physical Systems (CPS) is a multidimensional complex system that integrates computing, network and physical environment. Flight control system enables the aircraft to interact with the outside world and other components, which is a typical CPS. The Verification of safety has been a research hotspot. Model checking is a formal verification method with a high degree of automation, including three steps: model construction, properties specifications and execution. In general, Properties has been generated based on the subjective experience of the verifier, and there is a lack of a strict process. We focus on the process of establishing specifications using Failure mode and effect analysis (FMEA) And then use our improved method to do the requirement level model checking about the return process and collision prevention based on PX4 flight control system. The results prove that our method makes the model checking logic clearer and has good adaptability.

An Empirical Study of Flight Control System Model Checking Integrated with FMEA

The uncertainty of intelligence software makes it impossible for even developers to determine the output corresponding to each input. Therefore, it is particularly important to find a way to find intelligence software errors efficiently. Fuzzing testing technology is considered to be an effective technology for discovering software vulnerabilities, The selected test case generation strategy will affect its testing efficiency. In this article, we propose a counterexample-guided fuzzing method. According to the generated counterexamples, the scope of the sample space is continuously reduced, so as to find more counterexamples in a limited number of samples and improve the ability to uncover mistakes. In our research, The ability about revealing errors of test object has increased by 20%.

An Empirical Study of Counterexample-Guided Fuzzing for Neural Networks Verification

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