In the rapidly evolving business landscape, blockchain technology emerges as a key innovator, enhancing trust, transparency, and security. However, the unique features of blockchain pose challenges in developing blockchain-based software (BSD) systems, demanding improvements in conventional software development processes. This study aims to identify BSD process areas and develop a success probability prediction framework, enhancing BSD process success and progression. We conducted a comprehensive literature survey and a questionnaire-based survey with practitioners to identify BSD process areas and gather training data. The study employs the Grey Wolf Optimizer (GWO) combined with the Naive Bayes Classifier to create a success probability prediction framework for BSD processes. Our research identifies 47 BSD process areas, categorized across five software process improvement (SPI) stages: initial, managed, defined, quantitatively managed, and optimizing. The GWO algorithm facilitates the design of a predictive framework, assessing the success probability of each stage, encompassing various process areas. The framework also prioritizes process areas for each stage, helping practitioners identify critical areas considering implementation cost and success probability. Organizations using BSD can leverage this framework to improve their BSD processes. This study contributes to blockchain technology applications in software development, offering a systematic, predictive approach to augment the effectiveness and success rate of BSD processes.

Vision Language Models (VLMs), such as CLIP, are widely used for various multimodal tasks and offer significant advancements in image-text understanding. However, existing studies have revealed that VLMs inherit biases from their training data which lead to the reinforcement of harmful stereotypes and cultural misrepresentations. In the proposed work, we analyze the presence of biases associated with animals in the CLIP model. We introduce a novel taxonomy, called Animal Bias Taxonomy (ABT), which categorizes stereotyped associations of animals in three categories. We also curated an animal dataset from existing datasets and applied data-cleaning process on it to remove unwanted images. Using ABT, we evaluated the outputs of VLMs on animal dataset when prompted with animal-related stereotyped terms to assess whether CLIP propagates biased associations that align with cultural stereotypes. Our findings reveal that CLIP frequently exhibits skewed cultural interpretations, such as associating owls with wisdom. Our study underscores the necessity of bias evaluation in VLMs and calls for greater transparency and culturally diverse data curation to ensure fair and inclusive AI systems. The code is available at https://github.com/MohammadAnas5/Clip-sAnimalStereotyping

ABSTRACT DevOps is a collaborative software development process where practitioners work as a team to continuously develop, deploy, and deliver software. DevOps practices still need to be mature, and practitioners face numerous challenges while considering DevOps as a software development process. The mainstream research community has helped simplify the DevOps adoption process and eliminate complexities by developing DevOps maturity models. However, the current maturity frameworks cannot measure every component of DevOps and do not mention metrics as parameters for measuring different DevOps practices or features. Therefore, this study aims to identify metrics for measuring practices and activities responsible for DevOps implementation. The systematic literature review (SLR) method was used to determine the metrics needed to measure DevOps practices. Using SLR, we have identified 32 metrics from 57 articles. The metrics identified in this study can be used to measure the impact of the practices adopted for DevOps implementation within software development organizations. Furthermore, we divided the identified metrics into Dev and Ops categories and five significant categories based on the DevOps lifecycle. The classification of metrics in our study into diverse regions provides a conceptual framework and understanding of DevOps measures.

Recent years have witnessed tremendous advancements in Al tools (e.g., ChatGPT, GPT-4, and Bard), driven by the growing power, reasoning, and efficiency of Large Language Models (LLMs). LLMs have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. Despite their proficiency in general queries, specialized tasks such as metaphor understanding and fake news detection often require finely tuned models, posing a comparison challenge with specialized Deep Learning (DL). We propose an assessment framework to compare task-specific intelligence with general-purpose LLMs on suicide and depression tendency identification. For this purpose, we trained two DL models on a suicide and depression detection dataset, followed by testing their performance on a test set. Afterward, the same test dataset is used to evaluate the performance of four LLMs (GPT-3.5, GPT-4, Google Bard, and MS Bing) using four classification metrics. The BERT-based DL model performed the best among all, with a testing accuracy of 94.61%, while GPT-4 was the runner-up with accuracy 92.5%. Results demonstrate that LLMs do not outperform the specialized DL models but are able to achieve comparable performance, making them a decent option for downstream tasks without specialized training. However, LLMs outperformed specialized models on the reduced dataset.

Quantum computing (QC) holds the potential to revolutionize computing by solving complex problems exponentially faster than classical computers, transforming fields such as cryptography, optimization, and scientific simulations. To unlock the potential benefits of QC, quantum software development (QSD) enables harnessing its power, further driving innovation across diverse domains. To ensure successful QSD projects, it is crucial to concentrate on key variables. This study aims to identify key variables in QSD and develop a model for predicting the success probability of QSD projects. We identified key QSD variables from existing literature to achieve these objectives and collected expert insights using a survey instrument. We then analyzed these variables using an optimization model, i.e., Genetic Algorithm (GA), with two different prediction methods the Naïve Bayes Classifier (NBC) and Logistic Regression (LR). The results of success probability prediction models indicate that as the QSD process matures, project success probability significantly increases, and costs are notably reduced. Furthermore, the best fitness rankings for each QSD project variable determined using NBC and LR indicated a strong positive correlation (rs=0.945). The t-test results (t = 0.851, p = 0.402>0.05) show no significant differences between the rankings calculated by the two methods (NBC and LR). The results reveal that the developed success probability prediction model, based on 14 identified QSD project variables, highlights the areas where practitioners need to focus more in order to facilitate the cost-effective and successful implementation of QSD projects.

Abstract Increasingly, software organizations are implementing DevOps culture to benefit from it in terms of continuous testing, delivery, improvement, and so forth. Implementing DevOps is difficult due to a lack of understanding about the practices and their effective application for its effective implementation. This paper aims to explore different DevOps practices that can be implemented in software organizations. The study involves conducting a systematic literature review (SLR) to identify DevOps implementation practices, followed by the utilization of the fuzzy best–worst method (FBWM) to establish a taxonomy or classification of software practices. We have used an SLR to investigate the practices, and subsequently, the survey method was followed to validate the identified practices. Moreover, the best–worst method (BWM) was considered to evaluate the significance and develop the taxonomy of the practices. The results of this study extracted 19 practices that have been identified in the SLR process. The identified factors are further classified into six core DevOps lifecycle phases. The results of the BWM approach are shown. The outcomes of the study conclude that the proposed taxonomy of the practices could help DevOps practitioners and researchers effectively implement them in software development organizations.

Abstract Software development projects depend on collaborative teams. In the past 50 years of research, various studies have explored the effect of software engineer's personality traits and cultural values on team performance. These studies have led to better understand these relationships; however, how the personality traits and cultural values influence the team effectiveness is still far away in the literature. This research aims to investigate the relationships between social and psychological complexities (including personality traits and cultural values), team coordination, team motivation, and team success (which comprises team effectiveness and team climate) to explore the impact of social and psychological issues on software team success. An online survey targeting software development professionals and unstructured interviews were followed for data collection. We received 112 responses from software developers working in different countries. Findings indicate that personality traits and cultural values, that is, consciousness, openness, harmony, and autonomy, have positive relationship with team coordination effectiveness, while other factors such as neuroticism, embeddedness, hierarchy, and mastery were found to be related negatively with it. These negative relationships can be mitigated by motivating team members appropriately. Based on our research findings, we conclude that the negative impact caused by different personality and cultural traits could be reduced by improving team coordination effectiveness using effective motivation.

DevOps has become an increasingly popular approach to software development and operations. DevOps has evolved rapidly in recent years, with numerous maturity models being proposed to help organizations assess their level of adoption and identify improvement areas. However, there is no consensus on which model is the most effective, as different models may be more suited to different organizational contexts. This paper compares six popular DevOps maturity models using the Fuzzy TOPSIS Multiple Criteria Decision Making (MCDM) methods. Fuzzy TOPSIS is a popular MCDM technique that can handle imprecise and uncertain information. We analyze each model based on seven criteria: Culture, Automation, Continuous Integration/Continuous Delivery, Monitoring and Feedback, Security, Metrics and Measurement, and Continuous Learning and Improvement, and determine which model is the most suitable according to industry standards. The analysis revealed that Radstaak's DevOps maturity model outperforms the others when evaluated through seven standard criteria for assessing maturity models.

Currently, the software development organizations are adopting DevOps practices in order to develop quality product. Due to the lack of definition of DevOps, the principles, practices, and methods adopted in DevOps to determine success have changed substantially. There are several benefits of DevOps can be achieved if the DevOps practices are implemented effectively and efficiently. This study has been conducted to identify the DevOps practices that contribute to achieve a high level of DevOps. The qualitative and interpretive approach have been used for analyzing the HELENA2 dataset. The maturity is calculated using 36 DevOps practices based on the four different classifications: (1) rarely used DevOps, (2) sometimes used DevOps, (3) often used DevOps, and (4) always used DevOps with the help of degrees of addressing 18 goals. The maturity score for the different categories indicates that organizations that use DevOps always are more mature to achieve goals than organizations that use it rarely.

Abstract Planning and managing of requirement change management (RCM) process in global software development (GSD) are a complicated task, but the RCM plays a significant role in developing the quality software within time and budget. The key aim of this study is to prioritize the factors that could positively influence the RCM program in GSD context. To achieve the study objective, the questionnaire survey study was conducted to get the feedback of the practitioners concerning the success factors of RCM in GSD context. Moreover, the fuzzy analytical hierarchy process (FAHP) was applied. The application of FAHP is novel in this research domain as it has been effectively applied previously in various other research areas, for example, supplier selection, electronics and electrical, personnel selection, and agile software development. The results of this study will provide the prioritization‐based taxonomy of RCM success factors and also contribute by introducing the novel FAHP approach in the research domain of RCM in GSD. The FAHP approach assists the practitioners to reduce the uncertainty and vague opinions of RCM experts.