Context:　Bug　localization,　namely,　to　locate　suspicious　snippets　from　source　code　files　for　developers　to　fix　the　bug,　is　crucial　for　software　quality　assurance　and　software　maintenance.　Effective　bug　localization　technique　is　desirable　for　software　developers　to　reduce　the　effort　involved　in　bug　resolution.　State-of-the-art　bug　localization　techniques　concentrate　on　file-level　coarse-grained　localization　by　lexical　matching　bug　reports　and　source　code　files.　However,　this　would　bring　about　a　heavy　burden　for　developers　to　locate　feasible　code　snippets　to　make　change　with　the　goal　of　fixing　the　bug.　Objective:　This　paper　proposes　a　novel　approach　called　FineLocator　to　method-level　fine-grained　bug　localization　by　using　semantic　similarity,　temporal　proximity　and　call　dependency　for　method　expansion.　Method:　Firstly,　the　bug　reports　and　the　methods　of　source　code　are　represented　by　numeric　vectors　using　word　embedding　(word2vec)　and　the　TF-IDF　method.　Secondly,　we　propose　three　query　expansion　scores　as　semantic　similarity　score,　temporal　proximity　score　and　call　dependency　score　to　address　the　representation　sparseness　problem　caused　by　the　short　lengths　of　methods　in　the　source　code.　Then,　the　representation　of　a　method　with　short　length　is　augmented　by　elements　of　its　neighboring　methods　with　query　expansion.　Thirdly,　when　a　new　bug　report　is　incoming,　FineLocator　will　retrieve　the　methods　in　source　code　by　similarity　ranking　on　the　bug　report　and　the　augmented　methods　for　bug　localization.　Results:　We　collect　bug　repositories　of　ArgoUML,　Maven,　Kylin,　Ant　and　AspectJ　projects　to　investigate　the　performance　of　the　proposed　FineLocator　approach.　Experimental　results　demonstrate　that　the　proposed　FineLocator　approach　can　improve　the　performances　of　method-level　bug　localization　at　average　by　20%,　21%　and　17%　measured　by　Top-N　indicator,　MAP　and　MRR　respectively,　in　comparison　with　state-of-the-art　techniques.　Conclusion:　This　is　the　first　paper　to　demonstrate　how　to　make　use　of　method　expansion　to　address　the　representation　sparseness　problem　for　method-level　fine-grained　bug　localization.