Using Supervised Learning to Guide the Selection of Software Inspectors in Industry

Abstract

Software development is a multi-phase process that starts with requirement engineering. Requirements elicited from different stakeholders are documented in natural language (NL) software requirement specification (SRS) document. Due to the inherent ambiguity of NL, SRS is prone to faults (e.g., ambiguity, incorrectness, inconsistency). To find and fix faults early (where they are cheapest to find), companies routinely employ inspections, where skilled inspectors are selected to review the SRS and log faults. While other researchers have attempted to understand the factors (experience and learning styles) that can guide the selection of effective inspectors but could not report improved results. This study analyzes the reading patterns (RPs) of inspectors recorded by eye-tracking equipment and evaluates their abilities to find various fault-types. The inspectors' characteristics are selected by employing ML algorithms to find the most common RPs w.r.t each fault-types. Our results show that our approach could guide the inspector selection with an accuracy ranging between 79.3% and 94% for various fault-types.