LDR | | 02021nmm uu200397 4500 |
001 | | 000000332426 |
005 | | 20240805170753 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438206472 |
035 | |
▼a (MiAaPQ)AAI10785980 |
035 | |
▼a (MiAaPQ)rpi:11259 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Hathaway, Charles. |
245 | 10 |
▼a Broadening the Applicability of Software Complexity Metrics with Biological Analogues. |
260 | |
▼a [S.l.] :
▼b Rensselaer Polytechnic Institute.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 149 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
500 | |
▼a Advisers: Ron Eglash |
502 | 1 |
▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
520 | |
▼a Software metrics represent an application of conventional computer science to itself in an attempt to quantify the complexity of software systems. Many of the conventional metrics proposed in academia and industry find their roots in a top-down |
520 | |
▼a Chapters 5 and 6 compare taint analysis, measured using standard complexity metrics, with an ecosystem approach that examines interactive complexity. Chapter 3 examines the use of traditional complexity metrics to measure student comprehension o |
520 | |
▼a Combining these bio-inspired and traditional metrics utilizing machine learning techniques, also inspired by biology and neurology, chapter 7 demonstrates that greater results can be achieved with both angles than a single perspective. |
590 | |
▼a School code: 0185. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0984 |
710 | 20 |
▼a Rensselaer Polytechnic Institute.
▼b Computer Science. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0185 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997334
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |