Refining active learning to increase behavioral coverage

Research output: Contribution to conferenceAbstractAcademic

Abstract

Modern high-tech industry is dealing with the maintenance of complex software systems today which consist of a large number of interconnected software components. Many of these components become legacy over years due to lack of documentation and unavailability of original developers. Several techniques are available in literature to retrieve the behavioral models from the existing software. Among those, the dynamic analysis techniques analyze the actual execution of the software, either via execution traces (passive learning), or by interaction with the software components (active learning). These techniques cannot guarantee alone to learn the complete and correct software behavior due to the limitations of each technique. We present an approach to aid active learning technique with software logs (execution traces) and passive learning result to increase the behavioral coverage of learned models.
Original languageEnglish
Number of pages1
Publication statusPublished - 3 Oct 2018
EventACM Celebration of Women in Computing
womENcourage 2018
- Belgrade, Serbia
Duration: 3 Oct 20185 Oct 2018
https://womencourage.acm.org/2018/

Conference

ConferenceACM Celebration of Women in Computing
womENcourage 2018
CountrySerbia
CityBelgrade
Period3/10/185/10/18
Internet address

Fingerprint

Refining
Dynamic analysis
Industry
Problem-Based Learning

Cite this

Aslam, K., Luo, Y., Schiffelers, R. R. H., & van den Brand, M. G. J. (2018). Refining active learning to increase behavioral coverage. Abstract from ACM Celebration of Women in Computing
womENcourage 2018, Belgrade, Serbia.
Aslam, K. ; Luo, Y. ; Schiffelers, R.R.H. ; van den Brand, M.G.J. / Refining active learning to increase behavioral coverage. Abstract from ACM Celebration of Women in Computing
womENcourage 2018, Belgrade, Serbia.1 p.
@conference{8085f2e6262d484494865965af73a658,
title = "Refining active learning to increase behavioral coverage",
abstract = "Modern high-tech industry is dealing with the maintenance of complex software systems today which consist of a large number of interconnected software components. Many of these components become legacy over years due to lack of documentation and unavailability of original developers. Several techniques are available in literature to retrieve the behavioral models from the existing software. Among those, the dynamic analysis techniques analyze the actual execution of the software, either via execution traces (passive learning), or by interaction with the software components (active learning). These techniques cannot guarantee alone to learn the complete and correct software behavior due to the limitations of each technique. We present an approach to aid active learning technique with software logs (execution traces) and passive learning result to increase the behavioral coverage of learned models.",
author = "K. Aslam and Y. Luo and R.R.H. Schiffelers and {van den Brand}, M.G.J.",
year = "2018",
month = "10",
day = "3",
language = "English",
note = "ACM Celebration of Women in Computing<br/>womENcourage 2018 ; Conference date: 03-10-2018 Through 05-10-2018",
url = "https://womencourage.acm.org/2018/",

}

Aslam, K, Luo, Y, Schiffelers, RRH & van den Brand, MGJ 2018, 'Refining active learning to increase behavioral coverage' ACM Celebration of Women in Computing
womENcourage 2018, Belgrade, Serbia, 3/10/18 - 5/10/18, .

Refining active learning to increase behavioral coverage. / Aslam, K.; Luo, Y.; Schiffelers, R.R.H.; van den Brand, M.G.J.

2018. Abstract from ACM Celebration of Women in Computing
womENcourage 2018, Belgrade, Serbia.

Research output: Contribution to conferenceAbstractAcademic

TY - CONF

T1 - Refining active learning to increase behavioral coverage

AU - Aslam, K.

AU - Luo, Y.

AU - Schiffelers, R.R.H.

AU - van den Brand, M.G.J.

PY - 2018/10/3

Y1 - 2018/10/3

N2 - Modern high-tech industry is dealing with the maintenance of complex software systems today which consist of a large number of interconnected software components. Many of these components become legacy over years due to lack of documentation and unavailability of original developers. Several techniques are available in literature to retrieve the behavioral models from the existing software. Among those, the dynamic analysis techniques analyze the actual execution of the software, either via execution traces (passive learning), or by interaction with the software components (active learning). These techniques cannot guarantee alone to learn the complete and correct software behavior due to the limitations of each technique. We present an approach to aid active learning technique with software logs (execution traces) and passive learning result to increase the behavioral coverage of learned models.

AB - Modern high-tech industry is dealing with the maintenance of complex software systems today which consist of a large number of interconnected software components. Many of these components become legacy over years due to lack of documentation and unavailability of original developers. Several techniques are available in literature to retrieve the behavioral models from the existing software. Among those, the dynamic analysis techniques analyze the actual execution of the software, either via execution traces (passive learning), or by interaction with the software components (active learning). These techniques cannot guarantee alone to learn the complete and correct software behavior due to the limitations of each technique. We present an approach to aid active learning technique with software logs (execution traces) and passive learning result to increase the behavioral coverage of learned models.

M3 - Abstract

ER -

Aslam K, Luo Y, Schiffelers RRH, van den Brand MGJ. Refining active learning to increase behavioral coverage. 2018. Abstract from ACM Celebration of Women in Computing
womENcourage 2018, Belgrade, Serbia.