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Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in artificial intelligence (AI) and machine learning (ML). However, machine learning meets software engineering not only with promising potentials but also with some inherent challenges. Despite some recent research efforts, we still do not have a clear understanding of the challenges of developing ML-based applications and the current industry practices. Moreover, it is unclear where software engineering researchers should focus their efforts to better support ML application developers. In this paper, we report about a survey that aimed to understand the challenges and best practices of ML application development. We synthesize the results obtained from 80 practitioners (with diverse skills, experience, and application domains) into 17 findings outlining challenges and best practices for ML application development. Practitioners involved in the development of ML-based software systems can leverage the summarized best practices to improve the quality of their system. We hope that the reported challenges will inform the research community about topics that need to be investigated to improve the engineering process and the quality of ML-based applications.

During the initial stages of software development, the primary goal is to define precise and detailed requirements without concern for software realizations. Security constraints should be introduced then and must be based on the semantic aspects of applications, not on their software architectures, as it is the case in most secure development methodologies. In these stages, we need to identify threats as attacker goals and indicate what conceptual security defenses are needed to thwart these goals, without consideration of implementation details. We can consider the effects of threats on the application assets and try to find ways to stop them. These threats should be controlled with abstract security mechanisms that can be realized by security patterns (ASPs) , that include only the core functions of these mechanisms, which must be present in every implementation of them. An abstract security pattern describes a conceptual security mechanism that includes functions able to stop or mitigate a threat or comply with a regulation or institutional policy. We describe here the properties of ASPs and present a detailed example. We relate ASPs to each other and to Security Solution Frames, which describe families of related patterns. We show how to include ASPs to secure an application, as well as how to derive concrete patterns from them. Finally, we discuss their practical value, including their use in “security by design” and IoT systems design.

For effective vulnerability management, vulnerability and attack information must be collected quickly and efficiently. A security knowledge repository can collect such information. The Common Vulnerabilities and Exposures (CVE) provides known vulnerabilities of products, while the Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of common attributes and approaches employed by adversaries to exploit known weaknesses. Due to the fact that the information in these two repositories are not linked, identifying related CAPEC attack information from CVE vulnerability information is challenging. Currently, the related CAPEC-ID can be traced from the CVE-ID using Common Weakness Enumeration (CWE) in some but not all cases. Here, we propose a method to automatically trace the related CAPEC-IDs from CVE-ID using three similarity measures: TF–IDF, Universal Sentence Encoder (USE), and Sentence-BERT (SBERT). We prepared and used 58 CVE-IDs as test input data. Then, we tested whether we could trace CAPEC-IDs related to each of the 58 CVE-IDs. Additionally, we experimentally confirm that TF–IDF is the best similarity measure, as it traced 48 of the 58 CVE-IDs to the related CAPEC-ID.

The Computer Science industry suffers from a vivid gender gap. To understand this gap, Computational Thinking skills in Computer Science education are analyzed by binary gender roles using block-based programming languages such as Scratch since they are intuitive for beginners. Platforms such as Dr. Scratch, aid learners in improving their coding skills by earning a Computational Thinking score while supporting effective assessments of students' projects and fostering basic computer programming. Although previous studies have examined gender differences using Scratch programs, few have analyzed the Scratch project type's impact on the evaluation process when comparing genders. Herein, the influence of project type is analyzed using instances of 124 (62 male, 62 female) projects on the Scratch website. Initially, projects were categorized based on the user's gender and project type. Hypothetical testing of each case shows that the scoring system has a bias based on the project type. As gender differences appear by project type, the project type may significantly affect the gender gap in Computational Thinking scores. This study demonstrates the importance of incorporating the project type's effect into the Scratch projects' evaluation process when assessing gender differences.

In evaluating the learning achievement of programming-thinking skills, the method of using a rubric that describes evaluation items and evaluation stages is widely employed. However, few studies have evaluated the reliability, validity, and consistency of the rubrics themselves. In this study, we introduced a statistical method for evaluating the characteristics of rubrics using the goal question metric (GQM) method. Furthermore, we proposed a method for measuring four evaluation results and characteristics obtained from rubrics developed using this statistical method. Moreover, we showed and confirmed the consistency and validity of the statistical method using the GQM method of the resulting developed rubrics. We show how to verify the consistency and validity of the rubric using the GQM method.

Vulnerability and attack information must be collected to assess the severity of vulnerabilities and prioritize countermeasures against cyberattacks quickly and accurately. Common Vulnerabilities and Exposures is a dictionary that lists vulnerabilities and incidents, while Common Attack Pattern Enumeration and Classification is a dictionary of attack patterns. Direct identification of common attack pattern enumeration and classification from common vulnerabilities and exposures is difficult, as they are not always directly linked. Here, an approach to directly find common links between these dictionaries is proposed. Then, several patterns, which are combinations of similarity measures and popular algorithms such as term frequency–inverse document frequency, universal sentence encoder, and sentence BERT, are evaluated experimentally using the proposed approach. Specifically, two metrics, recall and mean reciprocal rank, are used to assess the traceability of the common attack pattern enumeration and classification identifiers associated with 61 identifiers for common vulnerabilities and exposures. The experiment confirms that the term frequency–inverse document frequency algorithm provides the best overall performance.

Background: Subjective well-being (SWB) is an essential indicator of successful aging. Although social connections enhance SWB among older adults, few interventions have integrated community-based approaches with information and communication technology (ICT). This study evaluated the Chofu–Digital–Choju (CDC) project, a multi-component community intervention fostering in-person and online social connections among community-dwelling older adults in urban Japan. Methods: This quasi-experimental study (January 2022 to March 2024) included community-dwelling older adults aged 65–84 years in Chofu City, Tokyo, Japan. The intervention consisted of online classes, community hubs as local third places, and community events. Baseline and follow-up data were collected using self-administered questionnaires. Propensity score matching (1:1) was used to reduce selection bias, and generalized estimating equations were applied to evaluate the intervention effects. The primary outcome was SWB (Cantril Ladder). The secondary outcomes included social isolation, neighborhood relationships, social participation, health literacy, psychological health, physical activity, and ICT use. Results: Among the 1599 participants who completed both surveys, 209 (13.1%) participated in at least one CDC intervention component. After propensity score matching, 195 pairs were analyzed. No significant interaction effect was observed for SWB (β = 0.08, 95% confidence interval [CI]: −0.20, 0.37; p = 0.565). However, a significant interaction effect favored the intervention group for Internet usage frequency (odds ratio = 1.53, 95% CI: 1.08, 2.16; p = 0.016). A significant borderline interaction was also observed in health literacy (β = 0.13, 95% CI: −0.00, 0.26; p = 0.056), which reached significance in covariate-adjusted sensitivity analysis (p = 0.044). Subgroup analyses revealed that community hub participants showed significant interaction effects in health literacy (p = 0.021) and a trend toward reduced depressive symptoms (p = 0.084). Conclusions: The CDC intervention did not improve SWB over 2 years but enhanced Internet use and supported health literacy and depressive symptoms, particularly among hub participants. Community-based, multi-component interventions that integrate online and in-person activities may foster digital inclusion and specific health behaviors. Although SWB did not change in this study, these proximal gains may serve as foundational steps for long-term improvement. The study protocol was preregistered in the UMIN Clinical Trials Registry (UMIN000051393; Registered on 21 June 2023).

The association between patterns of internet use for older adults’ well-being is unclear. We examined the association between the frequency and purpose of internet use and subjective well-being in older Japanese adults. We analyzed cross-sectional data from 2343 community-dwelling older adults (aged 65–84 years). Subjective well-being was measured using the World Health Organization Well-Being Index as a continuous score, and internet use was categorized by frequency and purpose. Hierarchical linear regression analysis was controlled for sociodemographic and health-related covariates. After full adjustment, only daily (B = 1.04, 95% CI [0.53, 1.56]) and dual-purpose use (i.e., for both practical and social communication purposes; B = 0.80, 95% CI [0.28, 1.31]) were independently associated with higher well-being. The analysis of the combined patterns further suggested that daily use was the primary factor. For older adults, regularity of internet use was more strongly associated with well-being than diversity of purpose. Daily integration appears to be a key factor for realizing benefits, suggesting that sustained practice is the foundational step in building the digital capital necessary for a flourishing later life. Longitudinal studies are needed to confirm these findings and untangle the causal relationship between sustained internet use and improved well-being among older adults.

Programming education for beginners often employs online judges. Although this helps improve coding skills, students may not obtain sufficient educational effects if the assignment is too difficult. Instead of presenting a model answer to an assignment, this paper proposes an approach to provide students with problems that have content and answer source code similar to the assignment. The effectiveness of our approach is evaluated via an intervention experiment in a university lecture course. The improvement in the number of correct answers is statistically significant compared to the same course offered in a different year without the proposed system. Therefore, the proposed approach should aid in the understanding of an assignment and enhance the educational effect.

In this review, a Systematic Literature Review (SLR) on Open Body of Knowledge (BOK) is presented. Moreover, the theoretical base to build a model for knowledge description was created, and it was found that there is a lack of guidelines to describe knowledge description because of the dramatically increasing number of requirements to produce an Open BOK, the difficulty of comparing related BOK contents, and the fact that reusing knowledge description is a very laborious task. In this sense, this review can be considered as a first step in building a model that can be used for describing knowledge description in Open BOK. Finally, in order to improve the educational context, a comparison among BOK, structure, and evolution is conducted.