  Data entry forms use completeness requirements to specify the fields that are
required or optional to fill for collecting necessary information from
different types of users.
  However, some required fields may not be applicable for certain types of
users anymore. Nevertheless, they may still be incorrectly marked as required
in the form; we call such fields obsolete required fields.
  Since obsolete required fields usually have not-null validation checks before
submitting the form, users have to enter meaningless values in such fields in
order to complete the form submission. These meaningless values threaten the
quality of the filled data. To avoid users filling meaningless values, existing
techniques usually rely on manually written rules to identify the obsolete
required fields and relax their completeness requirements. However, these
techniques are ineffective and costly. In this paper, we propose LACQUER, a
learning-based automated approach for relaxing the completeness requirements of
data entry forms. LACQUER builds Bayesian Network models to automatically learn
conditions under which users had to fill meaningless values. To improve its
learning ability, LACQUER identifies the cases where a required field is only
applicable for a small group of users, and uses SMOTE, an oversampling
technique, to generate more instances on such fields for effectively mining
dependencies on them. Our experimental results show that LACQUER can accurately
relax the completeness requirements of required fields in data entry forms with
precision values ranging between 0.76 and 0.90 on different datasets. LACQUER
can prevent users from filling 20% to 64% of meaningless values, with negative
predictive values between 0.72 and 0.91. Furthermore, LACQUER is efficient; it
takes at most 839 ms to predict the completeness requirement of an instance.
