Determining the Tested Classes with Software Metrics
Öz
Early detection and
correction of errors appearing in software projects reduces the risk of
exceeding the estimated time and cost. An efficient and effective test plan
should be implemented to detect potential errors as early as possible. In the
earlier phases, codes can be analyzed by efficiently employing software metric
and insight can be gained about error susceptibility and measures can be taken
if necessary. It is possible to classify software metric according to the time
of collecting data, information used in the measurement, type and interval of
the data generated. Considering software metric depending on the type and
interval of the data generated, object-oriented software metric is widely used
in the literature. There are three main metric sets used for software projects
that are developed as object-oriented. These are Chidamber & Kemerer, MOOD
and QMOOD metric sets. In this study, an approach for identifying the classes
that should primarily be tested has been developed by using the object-oriented
software metric. Then, this approach is applied for selected versions of the
project developed. According to the results obtained, the correct determination
rate of sum of the metrics method, which was developed to identify the classes
that should primarily be tested, is ranged between 55% and 68%. In the random
selection method, which was used to make comparisons, the correct determination
rate for identifying the classes that should primarily be tested is ranged
between 9.23% and 11.05%. In the results obtained using sum of the metrics
method, a significant rate of improvement is observed compared to the random
selection method.
Anahtar Kelimeler
Kaynakça
- [1] Tiftik N., Öztarak, H., Ercek, G., Özgün, S.; Sis-tem/Yazılım Geliştirme Sürecinde Doğrulama Faaliyetleri, 3. Ulusal Yazilim Mühendisliği Sempozyumu (UYMS’07), Ankara, 2007.
- [2] Song, O., Sheppard, M., Cartwright, M., and Mair, C.; Software Defect Association Mining and Defect Correction Effort Prediction, IEEE Transactions on Software Engineering, 2006, 32(2), pp. 69-82.
- [3] Fenton, N. and Ohlsson, N.; Quantitative Analysis of Faults and Failures in a Complex Software System, IEEE Transactions on Software Engineering, 2000, 26(8), pp. 797-814.
- [4] Xiaowei, W.; The Metric System about Software Maintenance”, 2011 International Conference of Information Technology, Computer Engineering and Mana-gement Sciences, Wuhan, 2011.
- [5] Kaur, A., Sandhu, P. S., Brar, A. S.; An Empirical Approach for Software Fault Prediction, 5th International Conference on Industrial and Information Systems, pp. 261–265, Mangalore, India, 2010.
- [6] Raymond, P. L., Weimer, B., Weimer, W. R.; Learning a Metric for Code Readability, IEEE Transactions of Software Engineering, 2010, 36(4), pp. 546-558.
- [7] Ogasawara, H., Yamada, A., Kojo, M.; Experiences of Software Quality Management Using Metrics through the Life-Cycle, 18th International Conference on Software Engineering, pp. 179–188, Berlin, 1996.
- [8] Chaumun, M., Kabaili, H., Keller, R., and Lustman, F.; Change Impact Model for Changeability Assessment in Object-Oriented Software Systems, Science of Computer Programming, Elsevier, 2002, 45(2-3), pp. 155-174.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yazarlar
Fatih Yücalar
CELÂL BAYAR ÜNİVERSİTESİ
0000-0002-1006-2227
Türkiye
Emin Borandağ
CELÂL BAYAR ÜNİVERSİTESİ
0000-0001-5553-2707
Türkiye
Yayımlanma Tarihi
29 Aralık 2017
Gönderilme Tarihi
26 Temmuz 2017
Kabul Tarihi
31 Ekim 2017
Yayımlandığı Sayı
Yıl 2017 Cilt: 13 Sayı: 4