Automatically　assessing　code　readability　is　a　relatively　new　challenge　that　has　attracted　growing　attention　from　the　software　engineering　community.　In　this　paper,　we　outline　the　idea　to　regard　code　readability　assessment　as　a　learning-to-rank　task.　Specifically,　we　design　a　pairwise　ranking　model　with　siamese　neural　networks,　which　takes　as　input　a　code　pair　and　outputs　their　readability　ranking　order.　We　have　evaluated　our　approach　on　three　publicly　available　datasets.　The　result　is　promising,　with　an　accuracy　of　83.5%,　a　precision　of　86.1%,　a　recall　of　81.6%,　an　F-measure　of　83.6%　and　an　AUC　of　83.4%.　©　2022　ACM.