Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

Abstract: In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, incomplete member profile fields in a member profile associated with member of an online social networking service are identified. Profile completion score weight values associated with the incomplete member profile fields in the member profile are then determined. Thereafter, the incomplete member profile fields in the member profile are ranked, based on the profile completion score weight values associated with each of the incomplete member profile fields. A list of one or more of the highest ranked incomplete member profile fields are then displayed, together with a prompt recommending the member to complete the identified incomplete member profile fields.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are directed to a Peer Driven Editing Engine (hereinafter “PDE Engine”) that updates a profile of a target member account in a professional social network service (hereinafter “social network”) based on information provided from other member accounts. The PDE Engine identifies a particular type of profile attribute of a member account in a social network. The PDE Engine determines a target member account qualifies for an update with the particular type of profile attribute. The PDE Engine generates a prompt based on the particular type of profile attribute. The PDE Engine generates profile update content based on the target member account's response to the prompt. The PDE Engine includes the particular type of profile attribute in the target member account according to the profile update content.

Abstract: Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, incomplete member profile fields in a member profile associated with member of an online social networking service are identified. Profile completion score weight values associated with the incomplete member profile fields in the member profile are then determined. Thereafter, the incomplete member profile fields in the member profile are ranked, based on the profile completion score weight values associated with each of the incomplete member profile fields. A list of one or more of the highest ranked incomplete member profile fields are then displayed, together with a prompt recommending the member to complete the identified incomplete member profile fields.

Abstract: A machine may be configured to generate a unit test based on production data. For example, the machine displays, in an interactive user interface, one or more input-output pairs obtained based on a production log file generated by a production system. Each input-output pair may include an input to a first unit of code executable on the production system and an output that corresponds to the input and that is generated as a result of executing the first unit of code on the production system using the input. The machine may detect a selection of an input-output pair from the one or more input-output pairs displayed in the interactive user interface. The input-output pair may include a particular input and a particular output that corresponds to the particular input. The machine may generate a unit test for testing a second unit of code based on the selected input-output pair.