| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000436498 |
| 005 | | 20200228144244 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392648919 |
| 035 | |
▼a (MiAaPQ)AAI27700894 |
| 035 | |
▼a (MiAaPQ)NCState_Univ18402036993 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Krishna Prasad, Rahul. |
| 245 | 10 |
▼a Learning Actionable Analytics in Software Engineering. |
| 260 | |
▼a [S.l.]:
▼b North Carolina State University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 179 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: B. |
| 500 | |
▼a Advisor: Vatsavai, Ranga |
| 502 | 1 |
▼a Thesis (Ph.D.)--North Carolina State University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Software analytics is routinely used by researchers and industrial practitioners for many diverse tasks. Large organizations such as Microsoft routinely make practice data-driven policy development where organizational policies are learned from an extensive analysis of large datasets. However, despite these successes, there exist some limitations to modern software analytic tools - 1. Lack of relevant data to perform the analytics, and 2. Lack of insightful analytics. This thesis attempts to highlight and offer potential solutions to these pressing problems. A premise with most of the prior work on software data analytics is that there exists data from which we can learn models. But this premise is not always satisfied. When local data is scarce, sometimes it is possible to use data collected from other projects. Researchers achieve this using transfer learning which seeks to transfer knowledge from some source project and apply it to a target project. Much of the transfer learning methodologies achieve this by using complex dimensionality transformations. However, these methodologies were seldom generalizable and needlessly complex. To address this, this thesis offers a very simple "bellwether" transfer learner. Given N data sets, we find one dataset that which produces the best predictions on all the other projects. We call this the "bellwether" data set. We show that these can then be used for all subsequent analytics. We explore the existence of Bellwethers in a number of domains within software analytics: (a) Code smells detection |
| 590 | |
▼a School code: 0155. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a North Carolina State University. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-05B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0155 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494686
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |