Search

Background and Objective: Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment. Methods: We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration. Results: We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/, allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey. Conclusions: Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment. [ABSTRACT FROM AUTHOR]

Maintaining software quality is a significant challenge as the complexity of software is increasing with the rise of the software industry. Software defects are a primary concern in complex modules, and predicting them in the early stages of the software development life cycle (SDLC) is difficult. Previous techniques to address this issue have not been very promising. We have proposed “A hybrid ensemble model for software defect prediction using AI‐based techniques with feature preservation” to overcome this problem. We have used the National Aeronautics and Space Administration (NASA) dataset from the PROMISE repository for testing and validation. By applying exploratory data analysis (EDA), feature engineering, scaling, and standardization, we found that the dataset is imbalanced, which can negatively affect the model's performance. To address this, we have used the Synthetic Minority Oversampling (SMOTE) technique and the edited nearest neighbor (ENN) (SMOTE‐ENN). We have also used recursive feature elimination cross‐validation (RFE‐CV) with a pipeline to prevent data leaking in CV and kernel‐based principal component analysis (K‐PCA) to minimize dimensionality and selectively relevant features. The reduced dimensional data is then given to the eXtreme Gradient Boosting (XGBoost) for classification, resulting in the hybrid‐ensemble (SMERKP‐XGB) model. The proposed SMERKP‐XGB model is better than previously developed models in terms of accuracy (CM1: 97.53%, PC1: 92.05%, and PC2: 97.45%, KC1:95.65%), and area under the receiver operating characteristic curve values (CM1:96.30%, PC1:98.30%, and PC2:99.30%: KC1: 93.54) and other evaluation criteria mentioned in the literature. [ABSTRACT FROM AUTHOR]

The transformation to a software-defined car and the necessary competence building in software pose severe problems and threats for established car manufacturers. This study examines the adaptation of German car manufacturers to new digital technologies with their own competence building in software and with external partnerships. The firm’s resource- and competence-based views are used as the theoretical foundation. The empirical analysis includes an explorative case study of two German automotive firms and their external cooperation partners. Case studies show that automotive firms have chosen different pathways to build digital competencies. They realise a firm-specific mixture of competence building by investing their own resources or by partnering with external cooperation partners. The results showed remarkable differences in competence levels and successes between the two firms. The main theoretical result of the explorative case studies is that the management of dependencies, building or dissolution of relationships with other firms, aspect of time, and aspect of firm organisation must be included as endogenous factors in the theoretical analysis of competence building. [ABSTRACT FROM AUTHOR]

Stakeholder-driven development has become significant in the software industry. As a result, product managers have taken on a crucial role in IT projects by considering the demands of stakeholders, business objectives and knowing available technological solutions. The theoretical section of this article provides an overview of the evolution and characteristics of the product manager role. This paper empirically explored the various expectations towards product managers through the example of the Hungarian software industry by the content analysis of job advertisements (n=212) from LinkedIn. The identified patterns show that the product manager role differs depending on the size of the company. The results show that as the size of a company increases, leadership and active listening skills become increasingly important. On the other hand, micro- and small enterprises demonstrate the highest level of responsibility regarding cooperation. The findings of this article can offer valuable guidance for future product managers. [ABSTRACT FROM AUTHOR]

Digitalization of data into electronic formats can transform the efficiency of an organization. Advances in software programming have enabled seamless conversion of manual workflows into electronic processes. We have described our experiences in the development of reliable and robust inter-connected electronic systems. In this article we have addressed the challenges in the development of electronic laboratory notebook software that follows a workflow in a biological laboratory. These challenges include: (i) linguistics: translation of ‘language of the scientist’ to the ‘language of the software designer’ and to the ‘language of the programmer’, (ii) limited knowledge of capabilities and limitations of technologies on either side (scientific and software) and (iii) lack of communication and documentation practices for capturing the requirements. Data integrity is the central regulatory requirement to a quality management system. Digitalization can address this challenge by addressing three major advantages – standardization of processes, increased compliance and enhanced efficiency. The observations made during the development processes of the present study can be applied to scientific software development and increasing requirements for collaboration of diverse cross-functional teams. [ABSTRACT FROM AUTHOR]

Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, R2 value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets. [ABSTRACT FROM AUTHOR]

This research sought to evaluate the impact of leadership on the organizational results of a software company in Nova Serrana - MG. A descriptive, qualitative and quantitative study was carried out with managers and employees, using an interview guide and an evaluative questionnaire. The results highlighted the importance of training for leadership and employee proactivity, as pointed out by the Systems Development Manager and the Technical Support Manager. There were unfavorable perceptions of employees in specific sectors. In the categories of profile, relationship and results, there was a harmony between managers and subordinates, with an emphasis on the relationship assuming a more contingency leadership approach. It was identified that the leadership profile directly impacts organizational results. For future research, it is suggested to validate the model with a larger sample and carry out more statistical studies, exploring leadership in other technology companies in the region. The proposal can also be replicated in software companies with different characteristics for qualitative and quantitative comparisons. [ABSTRACT FROM AUTHOR]

Introduction: Work motivation and innovative behavior play a vital role in the development of organizations, as they increase employee productivity and effectiveness. It also means the possibility of developing entrepreneurship, preparing them to be active for possible changes, their participation in the decision-making process and initiating action. The purpose of this research is to investigate the effectiveness of management games training on the work motivation and innovative behaviors of managers, who after familiarizing themselves with this technique and facing situations and using it in decision-making examine its relationship with work motivation and innovative behaviors. Method: The research method was a semi-experimental pre-test-post-test type with a control group. management games training was applied as an independent variable to determine its effect on work motivation and innovative behaviors of managers as dependent variables. The sample size includes 30 managers of Datin Software Company, who were randomly assigned in two groups of 15 people, and their entry criteria were having work experience and being in the team for more than 2 years. The research instrument included two self-reporting questionnaires of Hackman and Oldham's work motivation (1976) and Jansen's employees' innovative behavior (2000), which were administered to both groups before and after the intended intervention (Management Games training).Also, Data were analyzed mancova with SPSS23 software. Results: The results showed the effect of management games training on work motivation and innovative behaviors of managers (P [ABSTRACT FROM AUTHOR]

Monitoring technical debt (TD) is considered highly important for software companies, as it provides valuable information on the effort required to repay TD and in turn maintain the system. When it comes to TD repayment, however, developers are often overwhelmed with a large volume of TD liabilities that they need to fix, rendering the procedure effort demanding. Hence, prioritizing TD liabilities is of utmost importance for effective TD repayment. Existing approaches rely on the current TD state of the system; however, prioritization would be more efficient by also considering its future evolution. To this end, the present work proposes a practical approach for prioritization of TD liabilities by incorporating information retrieved from TD forecasting techniques, emphasizing on the class‐level granularity to provide highly actionable results. Specifically, the proposed approach considers the change proneness and forecasted TD evolution of software artifacts and combines it with proper visualization techniques, to enable the early identification of classes that are more likely to become unmaintainable. To demonstrate and evaluate the approach, an empirical study is conducted on six real‐world applications. The proposed approach is expected to facilitate developers better plan refactoring activities, in order to manage TD promptly and avoid unforeseen situations long term. [ABSTRACT FROM AUTHOR]

The software industry always requires high‐quality software. Code quality has been improved via refactoring and re‐refactoring and removing code smells. Refactoring is a technique to improve existing code by modifying inner structure, while code smells are indication that might have a wrong in code that causes a negative impact on the code quality. According to previous studies, the accuracy of code smell detection was not stable and could be improved by advanced algorithms. Additionally, the impact of refactoring on each of the quality attributes is unclear. This research proposes the use of Aquila Optimizer to detect code smells in addition to investigate the refactoring and re‐refactoring impact through ISO 9126 model. The experiment is conducted on five open‐sources Java projects to detect nine code smell types and measure the values of the ISO 9126 before and after the refactoring and re‐refactoring techniques to show the impact on code quality. The results showed that the average of detection accuracy is 95.18% and 95.68% for precision and recall, respectively. Moreover, the results in large projects are better than in the medium projects. The findings also show improvement in the values of the ISO 9126 model after applying refactoring and re‐refactoring. [ABSTRACT FROM AUTHOR]

Technical debt (TD) poses a significant risk in the software industry, representing the compromises made during software development that result in long-term consequences. To facilitate the management of TD, it is important to explore the causes of TD and the effects of the presence of TD on software projects. While the concept of TD has been investigated in several countries, no study has explored TD in the context of Saudi Arabia. In this paper, we aim to investigate the familiarity of the TD concept within the Saudi software practitioners and identifying the common causes and effects of TD in the Saudi software industry. We replicated a global survey called InsighTD, using the same survey instrument and the analysis procedure but collecting the data from software practitioners in Saudi Arabia. A total of 48 Saudi software practitioners completed the survey. The study findings indicate that the TD concept is not well recognized among the Saudi software practitioners (50% of the survey respondents have never heard of it before). Additionally, we identify the common causes and effects of TD from the perspective of Saudi software practitioners. The top causes of TD were tight-release deadlines, work pressure, lack of experience, and inappropriate project planning. On the other hand, the top effects of TD were increased maintenance effort/cost, low software quality, and delayed delivery of new versions. In conclusion, our findings support previous research regarding the most common TD causes and effects. In addition, they highlight some important factors in the Saudi software industry. [ABSTRACT FROM AUTHOR]

Space and cyber are two of the three elements of the triad that draw the most attention, but the critical role of the last element--special operations forces' (SOF) physical proximity--is [...]