The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Semasa pelaksanaan sistem perisian, data pelaksanaannya boleh direkodkan. Dengan mengeksploitasi sepenuhnya data ini, pengamal perisian boleh menemui model tingkah laku yang menerangkan pelaksanaan sebenar sistem perisian asas. Data pelaksanaan perisian tidak berstruktur yang direkodkan mungkin terlalu rumit, menjangkau beberapa hari, dsb. Menggunakan teknik penemuan sedia ada menghasilkan model seperti spageti tanpa struktur yang jelas dan tiada maklumat berharga untuk pemahaman. Bermula dari pemerhatian bahawa sistem perisian terdiri daripada satu set komponen logik, Liu et al. mencadangkan untuk menguraikan masalah penemuan tingkah laku perisian kepada masalah bebas yang lebih kecil dengan menemui model tingkah laku setiap komponen dalam [1]. Walau bagaimanapun, keberkesanan pendekatan yang dicadangkan tidak dinilai sepenuhnya dan dibandingkan dengan pendekatan sedia ada. Dalam kertas kerja ini, kami menilai kualiti (dari segi kefahaman/kerumitan) model tingkah laku komponen yang ditemui secara kuantitatif. Berdasarkan penilaian, kami menunjukkan bahawa pendekatan ini dapat mengurangkan kerumitan model yang ditemui dan memberikan pemahaman yang lebih baik.
Cong LIU
Shandong University of Technology
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Salinan
Cong LIU, "Quantitative Evaluation of Software Component Behavior Discovery Approach" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 1, pp. 117-120, January 2021, doi: 10.1587/transinf.2020MPL0001.
Abstract: During the execution of software systems, their execution data can be recorded. By fully exploiting these data, software practitioners can discover behavioral models describing the actual execution of the underlying software system. The recorded unstructured software execution data may be too complex, spanning over several days, etc. Applying existing discovery techniques results in spaghetti-like models with no clear structure and no valuable information for comprehension. Starting from the observation that a software system is composed of a set of logical components, Liu et al. propose to decompose the software behavior discovery problem into smaller independent ones by discovering a behavioral model per component in [1]. However, the effectiveness of the proposed approach is not fully evaluated and compared with existing approaches. In this paper, we evaluate the quality (in terms of understandability/complexity) of discovered component behavior models in a quantitative manner. Based on evaluation, we show that this approach can reduce the complexity of the discovered model and gives a better understanding.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020MPL0001/_p
Salinan
@ARTICLE{e104-d_1_117,
author={Cong LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Quantitative Evaluation of Software Component Behavior Discovery Approach},
year={2021},
volume={E104-D},
number={1},
pages={117-120},
abstract={During the execution of software systems, their execution data can be recorded. By fully exploiting these data, software practitioners can discover behavioral models describing the actual execution of the underlying software system. The recorded unstructured software execution data may be too complex, spanning over several days, etc. Applying existing discovery techniques results in spaghetti-like models with no clear structure and no valuable information for comprehension. Starting from the observation that a software system is composed of a set of logical components, Liu et al. propose to decompose the software behavior discovery problem into smaller independent ones by discovering a behavioral model per component in [1]. However, the effectiveness of the proposed approach is not fully evaluated and compared with existing approaches. In this paper, we evaluate the quality (in terms of understandability/complexity) of discovered component behavior models in a quantitative manner. Based on evaluation, we show that this approach can reduce the complexity of the discovered model and gives a better understanding.},
keywords={},
doi={10.1587/transinf.2020MPL0001},
ISSN={1745-1361},
month={January},}
Salinan
TY - JOUR
TI - Quantitative Evaluation of Software Component Behavior Discovery Approach
T2 - IEICE TRANSACTIONS on Information
SP - 117
EP - 120
AU - Cong LIU
PY - 2021
DO - 10.1587/transinf.2020MPL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2021
AB - During the execution of software systems, their execution data can be recorded. By fully exploiting these data, software practitioners can discover behavioral models describing the actual execution of the underlying software system. The recorded unstructured software execution data may be too complex, spanning over several days, etc. Applying existing discovery techniques results in spaghetti-like models with no clear structure and no valuable information for comprehension. Starting from the observation that a software system is composed of a set of logical components, Liu et al. propose to decompose the software behavior discovery problem into smaller independent ones by discovering a behavioral model per component in [1]. However, the effectiveness of the proposed approach is not fully evaluated and compared with existing approaches. In this paper, we evaluate the quality (in terms of understandability/complexity) of discovered component behavior models in a quantitative manner. Based on evaluation, we show that this approach can reduce the complexity of the discovered model and gives a better understanding.
ER -