Table of Contents

• What is software development productivity, anyway? (keynote)Gail C. Murphy. 1 [doi]
• Why so complicated? simple term filtering and weighting for location-based bug report assignment recommendationRamin Shokripour, John Anvik, Zarinah Mohd Kasirun, Sima Zamani. 2-11 [doi]
• Which work-item updates need your response?Debdoot Mukherjee, Malika Garg. 12-21 [doi]
• Bug report assignee recommendation using activity profilesHoda Naguib, Nitesh Narayan, Bernd Brügge, Dina Helal. 22-30 [doi]
• Asking for (and about) permissions used by Android appsRyan Stevens, Jonathan Ganz, Vladimir Filkov, Premkumar T. Devanbu, Hao Chen. 31-40 [doi]
• Retrieving and analyzing mobile apps feature requests from online reviewsClaudia Iacob, Rachel Harrison. 41-44 [doi]
• Gerrit software code review data from AndroidMurtuza Mukadam, Christian Bird, Peter C. Rigby. 45-48 [doi]
• Who does what during a code review? datasets of OSS peer review repositoriesKazuki Hamasaki, Raula Gaikovina Kula, Norihiro Yoshida, Ana Erika Camargo Cruz, Kenji Fujiwara, Hajimu Iida. 49-52 [doi]
• Why, when, and what: analyzing stack overflow questions by topic, type, and codeMiltiadis Allamanis, Charles A. Sutton. 53-56 [doi]
• Deficient documentation detection: a methodology to locate deficient project documentation using topic analysisJoshua Charles Campbell, Chenlei Zhang, Zhen Xu, Abram Hindle, James Miller. 57-60 [doi]
• Detecting API usage obstacles: a study of iOS and Android developer questionsWei Wang, Michael W. Godfrey. 61-64 [doi]
• Encouraging user behaviour with achievements: an empirical studyScott Grant, Buddy Betts. 65-68 [doi]
• Is programming knowledge related to age? an exploration of stack overflowPatrick Morrison, Emerson R. Murphy-Hill. 69-72 [doi]
• A discriminative model approach for suggesting tags automatically for stack overflow questionsAvigit K. Saha, Ripon K. Saha, Kevin A. Schneider. 73-76 [doi]
• Exploring activeness of users in QA forumsVibha Singhal Sinha, Senthil Mani, Monika Gupta. 77-80 [doi]
• A study of innovation diffusion through link sharing on stack overflowCarlos Gómez, Brendan Cleary, Leif Singer. 81-84 [doi]
• Making sense of online code snippetsSiddharth Subramanian, Reid Holmes. 85-88 [doi]
• Building reputation in StackOverflow: an empirical investigationAmiangshu Bosu, Christopher S. Corley, Dustin Heaton, Debarshi Chatterji, Jeffrey C. Carver, Nicholas A. Kraft. 89-92 [doi]
• An exploratory analysis of mobile development issues using stack overflowMario Linares Vásquez, Bogdan Dit, Denys Poshyvanyk. 93-96 [doi]
• Answering questions about unanswered questions of stack overflowMuhammad Asaduzzaman, Ahmed Shah Mashiyat, Chanchal K. Roy, Kevin A. Schneider. 97-100 [doi]
• Will my patch make it? and how fast?: case study on the Linux kernelYujuan Jiang, Bram Adams, Daniel M. Germán. 101-110 [doi]
• Linux variability anomalies: what causes them and how do they get fixed?Sarah Nadi, Christian Dietrich, Reinhard Tartler, Richard C. Holt, Daniel Lohmann. 111-120 [doi]
• The impact of tangled code changesKim Herzig, Andreas Zeller. 121-130 [doi]
• A dataset from change history to support evaluation of software maintenance tasksBogdan Dit, Andrew Holtzhauer, Denys Poshyvanyk, Huzefa H. Kagdi. 131-134 [doi]
• Apache commits: social network datasetAlexander C. MacLean, Charles D. Knutson. 135-138 [doi]
• Understanding the evolution of type-3 clones: an exploratory studyRipon K. Saha, Chanchal K. Roy, Kevin A. Schneider, Dewayne E. Perry. 139-148 [doi]
• An empirical study of the fault-proneness of clone mutation and clone migrationShuai Xie, Foutse Khomh, Ying Zou. 149-158 [doi]
• Intensive metrics for the study of the evolution of open source projects: case studies from apache software foundation projectsSantiago Gala-Pérez, Gregorio Robles, Jesús M. González-Barahona, Israel Herraiz. 159-168 [doi]
• A preliminary investigation of using age and distance measures in the detection of evolutionary couplingsAbdulkareem Alali, Brian Bartman, Christian D. Newman, Jonathan I. Maletic. 169-172 [doi]
• Search-based duplicate defect detection: an industrial experienceMehdi Amoui, Nilam Kaushik, Abraham Al-Dabbagh, Ladan Tahvildari, Shimin Li, Weining Liu. 173-182 [doi]
• A contextual approach towards more accurate duplicate bug report detectionAnahita Alipour, Abram Hindle, Eleni Stroulia. 183-192 [doi]
• Bug resolution catalysts: identifying essential non-committers from bug repositoriesSenthil Mani, Seema Nagar, Debdoot Mukherjee, Ramasuri Narayanam, Vibha Singhal Sinha, Amit Anil Nanavati. 193-202 [doi]
• The eclipse and mozilla defect tracking dataset: a genuine dataset for mining bug informationAhmed Lamkanfi, Javier Pérez, Serge Demeyer. 203-206 [doi]
• Mining source code repositories at massive scale using language modelingMiltiadis Allamanis, Charles A. Sutton. 207-216 [doi]
• Do software categories impact coupling metrics?Lucas Batista Leite De Souza, Marcelo de Almeida Maia. 217-220 [doi]
• The maven repository dataset of metrics, changes, and dependenciesSteven Raemaekers, Arie van Deursen, Joost Visser. 221-224 [doi]
• A historical dataset for the gnome ecosystemMathieu Goeminne, Maëlick Claes, Tom Mens. 225-228 [doi]
• A network of rails: a graph dataset of ruby on rails and associated projectsPatrick Wagstrom, Corey Jergensen, Anita Sarma. 229-232 [doi]
• The GHTorent dataset and tool suiteGeorgios Gousios. 233-236 [doi]
• Discovering, reporting, and fixing performance bugsAdrian Nistor, Tian Jiang, Lin Tan. 237-246 [doi]
• Improving bug localization using correlations in crash reportsShaohua Wang, Foutse Khomh, Ying Zou. 247-256 [doi]
• Testing principles, current practices, and effects of change localizationSteven Raemaekers, Gabriela F. Nane, Arie van Deursen, Joost Visser. 257-266 [doi]
• Fixing the 'out of sight out of mind' problem: one year of mood-based microblogging in a distributed software teamKevin Dullemond, Ben van Gameren, Margaret-Anne D. Storey, Arie van Deursen. 267-276 [doi]
• Communication in open source software development mailing listsAnja Guzzi, Alberto Bacchelli, Michele Lanza, Martin Pinzger, Arie van Deursen. 277-286 [doi]
• Tag recommendation in software information sitesXin Xia, David Lo, Xinyu Wang, Bo Zhou. 287-296 [doi]
• Using developer interaction data to compare expertise metricsRomain Robbes, David Röthlisberger. 297-300 [doi]
• Project roles in the apache software foundation: a datasetMegan Squire. 301-304 [doi]
• Apache-affiliated twitter screen names: a datasetMegan Squire. 305-308 [doi]
• Assisting code search with automatic query reformulation for bug localizationBunyamin Sisman, Avinash C. Kak. 309-318 [doi]
• Mining succinct and high-coverage API usage patterns from source codeJue Wang, Yingnong Dang, Hongyu Zhang, Kai Chen, Tao Xie, Dongmei Zhang. 319-328 [doi]
• Rendezvous: a search engine for binary codeWei Ming Khoo, Alan Mycroft, Ross Anderson. 329-338 [doi]
• An unabridged source code dataset for research in software reuseWerner Janjic, Oliver Hummel, Marcus Schumacher, Colin Atkinson. 339-342 [doi]
• The MSR cookbook: mining a decade of researchHadi Hemmati, Sarah Nadi, Olga Baysal, Oleksii Kononenko, Wei Wang, Reid Holmes, Michael W. Godfrey. 343-352 [doi]
• Happy birthday! a trend analysis on past MSR papersSerge Demeyer, Alessandro Murgia, Kevin Wyckmans, Ahmed Lamkanfi. 353-362 [doi]
• Replicating mining studies with SOFASGiacomo Ghezzi, Harald C. Gall. 363-372 [doi]
• A historical dataset of software engineering conferencesBogdan Vasilescu, Alexander Serebrenik, Tom Mens. 373-376 [doi]
• Automatically mining software-based, semantically-similar words from comment-code mappingsMatthew J. Howard, Samir Gupta, Lori L. Pollock, K. Vijay-Shanker. 377-386 [doi]
• Strategies for avoiding text fixture smells during software evolutionMichaela Greiler, Andy Zaidman, Arie van Deursen, Margaret-Anne D. Storey. 387-396 [doi]
• Contextual analysis of program logs for understanding system behaviorsQiang Fu, Jian-Guang Lou, Qingwei Lin, Rui Ding, Dongmei Zhang, Tao Xie. 397-400 [doi]
• A dataset for evaluating identifier splittersDavid Binkley, Dawn Lawrie, Lori L. Pollock, Emily Hill, K. Vijay-Shanker. 401-404 [doi]
• INVocD: identifier name vocabulary datasetSimon Butler, Michel Wermelinger, Yijun Yu, Helen Sharp. 405-408 [doi]
• Better cross company defect predictionFayola Peters, Tim Menzies, Andrian Marcus. 409-418 [doi]
• Using citation influence to predict software defectsWei Hu, Kenny Wong. 419-428 [doi]
• Revisiting software development effort estimation based on early phase development activitiesMasateru Tsunoda, Koji Toda, Kyohei Fushida, Yasutaka Kamei, Meiyappan Nagappan, Naoyasu Ubayashi. 429-438 [doi]