Abstract The growing number of software startups has witnessed an open debate on the suitability and appropriateness of commonly used software development methodologies, including agile software development methodologies and practices. Startups, for example, tend to focus on producing minimum viable product, which challenge the use of these methods and calls for bespoke adaptation of these practices to suit startups. Agile adoption is not easy for software startup teams due to unreadiness, inadequate preparation and weak structure of these teams, focusing only on small part of agile practices, and high uncertainty in essential requirements and proper technology. A review of the state‐of‐the‐art reports on limited number of studies that have investigated the adoption of agile methods and practices to best suit the requirements software startups. This study uses design science research methodology to address this gap and develop a guideline for agile adaptation specifically for software startups. The developed guideline was validated and improved with the participation of 23 experts from 7 software startup teams through survey questionnaires and open discussion. This guideline includes 13 recommendations, categorized into three sections: selection of agile methods and practices, preparation for adaptation, and the adaptation of agile methods and practices. Evaluation of the results shows the simplicity of understanding the guideline, its usefulness, and its support for the expected agility of the software development process.

Abstract Considering the increasing need for software projects, estimating software development efforts is essential and can lead to improved project delivery quality. Machine learning methods are widely used to improve the accuracy of estimation. The boosting method is an ensemble machine learning technique less used in this field. In this research, five boosting algorithms including Adaboost, Gradient boosting, XGBoost, LightGBM, and CatBoost were implemented with the hyperparameter tuning framework Optuna on the ISBSG database. The Optuna is a next‐generation optimization method for automatically tuning hyperparameters of algorithms. Six evaluation criteria MMRE, MdMRE, MAE, MSE, Pred(0.25), and SA were used to evaluate the findings. The results show that the hyperparameter automatic tuning by Optuna increases the accuracy of prediction provided by all five models. When the Catboost algorithm uses Optuna to tune its hyperparameters has made the best prediction among the five algorithms studied in this research. Using Optuna, compared to the case where the algorithm uses its default settings, the highest percentage of prediction improvement was observed in the XGBoost algorithm (except for the SA criterion). Based on the criteria of MMRE, Pred(0.25), and SA, this study has a better prediction than some relatively similar articles.