Recent developments in Artificial Intelligence (AI) have shifted the software development paradigm. Past studies demonstrated how effective AI can generate code for programming purposes. However, to our knowledge, no prior study has been done to evaluate the effectiveness of SQL queries generated by AI. We utilized nine AI assistants to generate SQL queries. Our results reveal that most AI assistants generate inaccurate SQL queries, and based on the results, we provide possible implications for SQL developers.

BACKGROUND: DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA. Very few proteins, however, are DNA-binding in nature. Therefore, it is necessary to develop an efficient predictor for identifying DNA-BPs. RESULT: In this work, we have proposed new benchmark datasets for the DNA-binding protein prediction problem. We discovered several quality concerns with the widely used benchmark datasets, PDB1075 (for training) and PDB186 (for independent testing), which necessitated the preparation of new benchmark datasets. Our proposed datasets UNIPROT1424 and UNIPROT356 can be used for model training and independent testing respectively. We have retrained selected state-of-the-art DNA-BP predictors in the new dataset and reported their performance results. We also trained a novel predictor using the new benchmark dataset. We extracted features from various feature categories, then used a Random Forest classifier and Recursive Feature Elimination with Cross-validation (RFECV) to select the optimal set of 452 features. We then proposed a stacking ensemble architecture as our final prediction model. Named Stacking Ensemble Model for DNA-binding Protein Prediction, or StackDPP in short, our model achieved 0.92, 0.92 and 0.93 accuracy in 10-fold cross-validation, jackknife and independent testing respectively. CONCLUSION: StackDPP has performed very well in cross-validation testing and has outperformed all the state-of-the-art prediction models in independent testing. Its performance scores in cross-validation testing generalized very well in the independent test set. The source code of the model is publicly available at https://github.com/HasibAhmed1624/StackDPP . Therefore, we expect this generalized model can be adopted by researchers and practitioners to identify novel DNA-binding proteins.

Debugging is one of the most expensive and time-consuming processes in software development. To support programmers, researchers, and developers have introduced a wide variety of debuggers or tools to automatically find errors in code, to make this process more efficient. However, there is a gap between industry developers and students regarding the skillful use of debuggers. We aim to understand this gap by studying barriers that hinder new programmers from using debuggers. We conducted a survey involving 73 students with various extents of programming experience and performed qualitative analysis. The goal was to extract insights into why students do not develop debugger usage skills. Our results suggest the general lack of academic course focus on debuggers is one of the primary reasons for avoidance. At the same time, complex user interfaces and a lack of visualization also seem intimidating for many students, making using a debugger unappealing. Based on the results, we provide guidelines to motivate future debugger designs and education materials to improve debugger usage. Our survey results summary is publicly available at https://github.com/minhyukko/vlhcc23

Context: Kubernetes has emerged as the de-facto tool for automated container orchestration. Business and government organizations are increasingly adopting Kubernetes for automated software deployments. Kubernetes is being used to provision applications in a wide range of domains, such as time series forecasting, edge computing, and high-performance computing. Due to such a pervasive presence, Kubernetes-related security misconfigurations can cause large-scale security breaches. Thus, a systematic analysis of security misconfigurations in Kubernetes manifests, i.e., configuration files used for Kubernetes, can help practitioners secure their Kubernetes clusters. Objective: The goal of this paper is to help practitioners secure their Kubernetes clusters by identifying security misconfigurations that occur in Kubernetes manifests . Methodology: We conduct an empirical study with 2,039 Kubernetes manifests mined from 92 open-source software repositories to systematically characterize security misconfigurations in Kubernetes manifests. We also construct a static analysis tool called Security Linter for Kubernetes Manifests ( SLI-KUBE ) to quantify the frequency of the identified security misconfigurations. Results: In all, we identify 11 categories of security misconfigurations, such as absent resource limit, absent securityContext , and activation of hostIPC . Specifically, we identify 1,051 security misconfigurations in 2,039 manifests. We also observe the identified security misconfigurations affect entities that perform mesh-related load balancing, as well as provision pods and stateful applications. Furthermore, practitioners agreed to fix 60% of 10 misconfigurations reported by us. Conclusion: Our empirical study shows Kubernetes manifests to include security misconfigurations, which necessitates security-focused code reviews and application of static analysis when Kubernetes manifests are developed.

The ubiquitous usage of robots in modern society necessitates secure development of robotics systems. Practitioners who engage in robot development can benefit from a systematic study that investigates the categories of vulnerabilities that appear in robotics systems. The goal of this paper is to help practitioners mitigate vulnerabilities in robotics systems by conducting an empirical study of vulnerabilities in robotics systems. We conduct an empirical study where we analyze 176 robotics-related vulnerabilities collected from the Robot Vulnerability Database (RVD). Our findings show that: (i) robotics-related vulnerabilities can be classified into nine categories; (ii) memory-related vulnerabilities are the most frequent category, (iii) 92.6% of the reported vulnerabilities are software-related, and (iv) software components in robotics systems include more critical vulnerabilities compared to that of hardware components. Based on our findings, we provide a list of development activities that can be used to mitigate vulnerabilities for robotics systems.

This Innovative Practice, work in progress (WIP) paper presents our experience related to two exercises that focus on automated security static analysis, a practice used to integrate security into development and operations (DevOps). The concept has gained popularity amongst information technology (IT) organizations. However, security-related concerns, such as security weaknesses in DevOps artifacts can cause serious consequences. Our preliminary findings indicate that (i) students positively perceive the introduced exercises; and (ii) the students perform well if they are provided necessary background on the exercises. Our WIP paper lays the groundwork to build course materials that will facilitate development, deployment, and dissemination of DevOps-related education materials that also incorporate cybersecurity concepts.