• Publications
  • Influence
Effectiveness of Human Error Taxonomy during Requirements Inspection: An Empirical Investigation
TLDR
Results show that subjects using HET were not only more effective at detecting faults, but they found faults faster, and post-hoc analysis of HET revealed meaningful insights into the most commonly occurring human errors at different points during requirements development.
Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach
TLDR
This work aims at automation of fault-consolidation step by using supervised machine learning algorithms that can effectively isolate faults from non-faults during the fault consolidation step of requirements inspections.
Using a Cognitive Psychology Perspective on Errors to Improve Requirements Quality: An Empirical Investigation
TLDR
A newly developed Human Error Taxonomy (HET) and a formal Error-Abstraction and Inspection (EAI) process to improve fault detection performance of inspectors during the requirements inspection and provide useful insights into commonly occurring human errors that contributed to requirement faults are described.
Incorporating Human Error Education into Software Engineering Courses via Error-based Inspections
TLDR
An exploratory study to evaluate whether requirements inspections driven by human errors can be used to deliver both requirements validation knowledge and human error knowledge to students suggests that human error based inspections can enhance the fault detection abilities of students.
Defect Prevention in Requirements Using Human Error Information: An Empirical Study
TLDR
The results of this study show that a better understanding of human errors does lead developers to insert fewer problems into their own requirements documents, and indicate that different types of Human Error information have different impacts on fault prevention.
Using software metrics for predicting vulnerable classes and methods in Java projects: A machine learning approach
TLDR
A comparative study is described on how the selected metrics perform at different granularity levels can help the developers in choosing the appropriate metrics (at the desired level of granularity) and provide evidence for their usefulness during vulnerability prediction.
Detection of Requirement Errors and Faults via a Human Error Taxonomy: A Feasibility Study
TLDR
The Human Error Taxonomy is effective for identifying and classifying requirements errors and faults, thereby helping to improve the overall quality of the SRS and the software.
Using human error information for error prevention
TLDR
Evaluating whether understanding human errors contributes to the prevention of errors and concomitant faults during requirements engineering and identifying error prevention techniques used in industrial practice showed that the better a requirements engineer understands human errors, the fewer errors and Concomitant Fault makes when developing a new requirements document.
Usefulness of a Human Error Identification Tool for Requirements Inspection: An Experience Report
TLDR
This empirical study investigates the effectiveness of a newly developed Human Error Abstraction Assist (HEAA) tool in helping inspectors identify human errors to guide the fault detection during the requirements inspection.
...
1
2
3
4
...