4 results about "Agile software development" patented technology

The application discloses a large-scale agile software project scheduling method based on deep reinforcement learning, and comprises the following steps: initializing state information of agile software projects and human resources in a large-scale agile software project scheduling environment; establishing a large-scale agile software project scheduling decision model based on deep reinforcement learning to generate a scheduling scheme for scheduling; collecting trajectory information generated in the scheduling process, storing the trajectory information into an experience replay pool and updating priorities of all trajectories; when the number of trajectories in the experience replay pool reaches a requirement, training parameters of the scheduling decision model by batch sampling trajectories according to the priorities; generating a better scheduling scheme by using the trained scheduling decision model for scheduling; and outputting an optimal scheduling scheme if the total value of developed user stories is stable. The application can generate an optimal scheduling scheme in time according to changes in the environment state in response to random dynamic events or uncertain factors in the agile software development process.

To obtain story points for agile software development tasks more quickly and at a lower cost. [Solution] According to one aspect of the present disclosure, an information processing system is provided. The information processing system has at least one control unit. The control unit determines the story points of a task based on the difficulty level of the task and the estimated time for the task related to agile development software.

A method in an illustrative embodiment comprises configuring a software development process to include a plurality of features in respective distinct feature domains, and for each of the features, decomposing the feature into a plurality of epics, determining a number of sprints associated with each of the epics of the feature, determining a total number of sprints across all of the epics of the feature, adjusting the total number of sprints based at least in part on historical data, and automatically generating an estimate for completion of the feature based at least in part on the adjusted total number of sprints. One or more characteristics of the software development process are controlled based at least in part on the estimates generated for respective ones of the features. The software development process in some embodiments is illustratively an agile software development process implemented in a continuous integration / continuous deployment (CI / CD) system.

A system for effort estimation in agile software development using multi-stage evolutionary stacking, consisting of: a data acquisition module configured to retrieve software effort records from one or more data set repositories containing historical data from software development projects with characteristics and actual effort values; a data preprocessing module that is operationally connected to the data acquisition module and is configured to receive the aforementioned software effort data sets from the data acquisition module, cleans the received data by removing inconsistencies with missing target values, and normalizes numerical input characteristics to a common range; a first-level ensemble module connected to the data preprocessing module, wherein the first-level ensemble module comprises a variety of heterogeneous basic learners, including a Random Forest model, a Support Vector Regression model, and an Extreme Gradient Boosting model, which generate predictions from each of the heterogeneous basic learners using the preprocessed data sets received from the data preprocessing module; A genetic algorithm optimization module connected to the first layer's ensemble module, configured to: encode weights as a normalized real-valued vector for each of the heterogeneous base learners; apply a fitness function to minimize the mean squared validation error and derive an optimal weight vector; assign optimized weights to the predictions of each of the heterogeneous base learners; and generate weighted predictions based on the optimized weights. a second-level meta-learning module connected to the optimization module of the genetic algorithm, configured to receive the weighted predictions from the optimization module of the genetic algorithm, processes the weighted predictions using a deep multilayer perceptron neural network to learn complex patterns and nonlinear interactions, and generates a final effort estimate for the software; an output processing module connected to the second-level meta-learning module, configured to receive the final effort estimate for the software and process and visualize the data to improve user understanding; and a user interface connected to the output processing module to receive the processed final effort estimate for the software, wherein the user interface is configured to display the processed and visualized final effort estimate for the software.