Improving IR‐based traceability recovery via noun‐based indexing of software artifacts

Capobianco, Giovanni; Lucia, Andrea De; Oliveto, Rocco; Panichella, Annibale; Panichella, Sebastiano

doi:10.1002/smr.1564

Cited by 61 publications

(54 citation statements)

References 60 publications

(117 reference statements)

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“…Most proposed approaches for automatic bug assignment recommendations remove noise by only using general preprocessing steps of the natural language processing (NLP), such as removing stop words and non-alphabetic tokens. However, Capobianco et al [18] showed that using only unigram noun terms significantly improves the accuracy of IR-based traceability recovery method. Unlike other parts of speech, such as verbs and adjectives, nouns are usually used in a specific context.…”

Section: Extracted Entitiesmentioning

confidence: 99%

Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation

Shokripour

Anvik

Kasirun

et al. 2013

2013 10th Working Conference on Mining Software Repositories (MSR)

108

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Abstract-Large software development projects receive many bug reports and each of these reports needs to be triaged. An important step in the triage process is the assignment of the report to a developer. Most previous efforts towards improving bug report assignment have focused on using an activity-based approach. We address some of the limitations of activity-based approaches by proposing a two-phased location-based approach where bug report assignment recommendations are based on the predicted location of the bug. The proposed approach utilizes a noun extraction process on several information sources to determine bug location information and a simple term weighting scheme to provide a bug report assignment recommendation. We found that by using a location-based approach, we achieved an accuracy of 89.41% and 59.76% when recommending five developers for the Eclipse and Mozilla projects, respectively.

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Section: Extracted Entitiesmentioning

confidence: 99%

Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation

Shokripour

Anvik

Kasirun

et al. 2013

2013 10th Working Conference on Mining Software Repositories (MSR)

108

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“…The IR-based framework is widely used and the POS tagging technique has demonstrated to be effective for improving the performance [5,24]. Tian et al have investigated the effectiveness of seven POS taggers on sampled bug reports; the Stanford POS tagger and TreeTagger achieved the highest accuracy up to 90.5% [26].…”

Section: Part-of-speech Taggingmentioning

confidence: 99%

Augmenting Bug Localization with Part-of-Speech and Invocation

Zhou

Tong

Chen

et al. 2017

Int. J. Soft. Eng. Knowl. Eng.

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Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of our approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e., ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

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“…Dalam pencarian literatur yang telah ditemukan, kami menemukan banyak variasi-variasi pendekatan information retrieval. Misalnya penggunaan smoothing filtering pada IR [9], penggunaan noun-based indexing pada IR [10], Incremental LSI pada IR [11].…”

Section: Diskusiunclassified

Evaluasi Pendekatan Pembangunan Traceability Link Dalam Evolusi Perangkat Lunak

Fitria¹,

Aini²

2013

JUTI

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ABSTRAK PENDAHULUANTraceability (ketelusuran) adalah properti dari desain dan pengembangan perangkat lunak yang menghubungkan setiap artefak abstrak dengan artefak teknis maupun sebaliknya [1]. Pada proyek perangkat lunak dalam skala besar, traceability sangat penting untuk mengetahui ketelusuran antar artefak dalam fase-fase yang berbeda (analisis kebutuhan, analisis desain, dan analisis implementasi). Selain itu, traceability dapat mengetahui ketelusuran antara artefak dan pihak pengembang yang terlibat. Proses pembangunan ketelusuran yang dilakukan secara manual akan menghabiskan banyak waktu dan sangat berpotensi terjadi kesalahan sehingga informasi menjadi tidak lengkap. Oleh karena itu, sebuah sistem traceability secara otomatis diperlukan untuk membangun ketelusuran antar artefak.Penelitian tentang pendekatan untuk membangan traceability telah dimulai sejak tahun 1984 oleh Dorfman dan Flynn dengan membangun kakas untuk traceability dengan nama ART (Automated Requirements Traceability) [2]. Hingga saat ini, penelitian traceability masih terbuka dan masih memiliki banyak tantangan, terutama dalam meminimalisasi kesalahan keterhubungan antarartefak [3]. Penelitian yang dilakukan oleh Siti Rochimah, dkk pada tahun 2007 mengevaluasi berbagai pendekatan untuk traceability dengan menggunakan kerangka kerja taksonomi evolusi perangkat lunak yang diusulkan oleh Buckley [4].Tujuan penelitian ini adalah untuk melanjutkan evaluasi pendekatan yang berkembang untuk membangun traceability link dengan menggunakan kerangka kerja taksonomi seperti yang telah dilakukan oleh Siti [5]. Evaluasi berfokus untuk mengetahui kemampuan pendekatan dalam upaya mendukung evolusi perangkat lunak. Hasil evaluasi dapat digunakan untuk mengidentifikasi bagaimana pendekatan pembangunan traceability link yang berkembang dapat mendukung evolusi perangkat lunak. Berdasarkan hasil evaluasi tersebut, peneliti berikutnya dapat mengetahui gambaran besar dan kriteria yang dibutuhkan untuk mengembangkan pendekatan traceability yang lebih baik.Makalah ini disusun dalam lima subbab. Subbab pertama adalah pendahuluan. Subbab kedua adalah kajian pustaka mengenai metode pendekatan traceability. Subbab ketiga adalah penjelasan tentang teknik evaluasi. Subbab keempat adalah evaluasi metode pendekatan traceability. Subbab kelima adalah rangkuman dari semua hasil evaluasi. Subbab keenam menyampaikan tentang kesimpulan. EKSPLORASI LITERATUR PENDEKATAN TERBARU MENGENAI TRACEABILITY LINKPenelitian ini mencakup proses eksplorasi literatur sejumlah pendekatan untuk membangun traceability link. Literatur diperoleh dari jurnal Sciencedirect dan IEEE. Subbab ini menjelaskan secara garis besar mengenai beberapa pendekatan terbaru dalam hal tersebut.

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Improving IR‐based traceability recovery via noun‐based indexing of software artifacts

Cited by 61 publications

References 60 publications

Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation

Why so complicated? Simple term filtering and weighting for location-based bug report assignment recommendation

Augmenting Bug Localization with Part-of-Speech and Invocation

Evaluasi Pendekatan Pembangunan Traceability Link Dalam Evolusi Perangkat Lunak

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