Constraints Based Approach to Interactive Feature Location

Fujioka, Daiki; Nitta, Naoya

doi:10.1109/icsme.2017.55

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Gethers et al proposed an approach, which combines different techniques, such as information retrieval, dynamic analysis, and software repository mining together to perform feature location [33]. Daiki et al combined both static and dynamic constraints in object-oriented programs, and performed an interactive feature location process [34].…”

Section: B Feature Locationmentioning

confidence: 99%

Enabling Feature Location for API Method Recommendation and Usage Location

Sun

et al. 2019

IEEE Access

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Given a new feature request during software evolution, developers are used to employing existing third-party libraries and APIs for implementation. However, it is usually non-trivial to find suitable APIs and to decide where to use these APIs in the original software. In this paper, we develop an approach for recommending API methods and usage locations through mining various software repositories. First, we analyze software repositories and use the feature location technique to localize feature-related files as API usage locations. Then, we utilize the feature-related files and API libraries to identify potential API methods for the implementation of the incoming feature request. We evaluate our approach on 5000 feature requests selected from five Java projects, and the results demonstrate that our approach can achieve 29.7% and 9.6% improvement in terms of Hit@5 and Hit@10 in recommending API methods, compared with the existing approach. For API usage localization, our approach can get MAP, MRR, Hit@1, HIt@5, and Hit@10 values with 0.293, 0.434, 0.303, 0.602, and 0.685, respectively.

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Section: B Feature Locationmentioning

confidence: 99%

Enabling Feature Location for API Method Recommendation and Usage Location

Sun

et al. 2019

IEEE Access

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“…When maintaining or reusing extensive software, much effort is often required to understand how a feature is applied in existing SC. To tackle the problem, extensive studies were conducted on FL methods, which can efficiently identify the part of the SC related to a feature 4 …”

Section: Introductionmentioning

confidence: 99%

An efficient scheme for secure feature location using data fusion and data mining in internet of things environment

Balaji¹,

Lakshmi²,

Anand

et al. 2020

Softw Pract Exp

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Summary Feature location (FL) is performed to find the relationships between domain concepts and other software artifacts. One major problem in maintaining a software system is to understand how many functional features exist in a system and how these features are implemented. Also, poor security is the prime problem in the FL system. However, the existing recent FL techniques use a textual and dynamic approach, which is not found to be secure, keeping in view the changes in the description of security attacks. To overcome this drawback, this work proposed a novel secure approach for FL utilizing data fusion as well as data mining for the internet of things environment. Firstly, the repeated test cases (TC) are eradicated as of the labeled TC. Next, important attributes are selected using the artificial flora optimization algorithm from the removed labeled TC. Then, association rule mining is performed to ascertain closed attributes. Subsequently, encrypt the closed attributes utilizing Caesar Cipher‐Rivest, Shamirs, as well as Adelman algorithm. After that, the score value of the closed attributes counts was found utilizing entropy calculation. Finally, the score value is given as input to the normalized‐K‐Means (N‐[K‐Means]) algorithm, where the score value is normalized utilizing min‐max normalization and then grouped utilizing K‐Means algorithm (KMA). It proffers better results for FL in the source code. The proposed N‐(K‐Means) performance is found better in comparison to the KMA and latent semantic indexing methods. The proposed system proffered better FL results in comparison to the other prevailing methods.

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Reviewing Dynamic Feature Location Techniques: Basic Elements and Challenges

Buzaid

Hammad

2021

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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Constraints Based Approach to Interactive Feature Location

Cited by 4 publications

References 14 publications

Enabling Feature Location for API Method Recommendation and Usage Location

Enabling Feature Location for API Method Recommendation and Usage Location

An efficient scheme for secure feature location using data fusion and data mining in internet of things environment

Reviewing Dynamic Feature Location Techniques: Basic Elements and Challenges

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