Planning for the next software release using adaptive network-based fuzzy inference system

Abstract

The release planning process concerns with assigning requirements to the different future releases of the software. This paper considers three factors that govern the release planning process: stakeholders' satisfaction, risk, and availability of resources. All of these factors depend on human knowledge, which is always incomplete, imprecise, and approximated. This classifies release planning as an under-uncertainty decision-making problem. This paper proposes a prioritization approach for generating a release plan for the next release of the software. The proposed approach employs a fuzzy inference system engine in order to tackle the uncertainty in the release planning process. The artifacts of the fuzzy inference (FIS) process (the membership functions and the IF-rules) are constructed using adaptive network-based fuzzy inference system (ANFIS). ANFIS helps to reinforce the human knowledge with the knowledge obtained from the historical data. Experiments show that the outputs of the proposed framework are affected by the reliability, accuracy, and the orientation of the historical data used to train the ANFIS module. For example, when training the ANFIS module using data that concentrates on the factor of stakeholders' satisfaction, the proposed framework has shown very good results from the perspective of this factor.