author = {Andrea Capiluppi and Alexander Serebrenik and Leif Singer},
  title = {{A}ssessing {T}echnical {C}andidates on the {S}ocial {W}eb},
  journal = {{IEEE} {S}oftware},
  year = {2013},
  volume = {30},
  pages = {45-51},
  number = {1},
  month = {jan.-feb.},
  abstract = {The Social Web provides comprehensive and publicly available information
	about software developers: they can be identified as contributors
	to open source projects, as experts at maintaining weak ties on social
	network sites, or as active participants to knowledge sharing sites.
	These signals, when aggregated and summarized, could be used to define
	individual profiles of potential candidates: job seekers, even if
	lacking a formal degree or changing their career path, could be qualitatively
	evaluated by potential employers through their online contributions.
	At the same time, developers are aware of the Web's public nature
	and the possible uses of published information when they determine
	what to share with the world. Some might even try to manipulate public
	signals of technical qualifications, soft skills, and reputation
	in their favor. Assessing candidates on the Web for technical positions
	presents challenges to recruiters and traditional selection procedures;
	the most serious being the interpretation of the provided signals.
	Through an in-depth discussion, we propose guidelines for software
	engineers and recruiters to help them interpret the value and trouble
	with the signals and metrics they use to assess a candidate's characteristics
	and skills.},
  doi = {10.1109/MS.2012.169},
  issn = {0740-7459},
  url = {}