An empirical study of factors affecting cross-project aging-related bug prediction with TLAP

Software aging is a phenomenon in which long-running software systems show an increasing failure rate and/or progressive performance degradation. Due to their nature, Aging-Related Bugs (ARBs) are hard to discover during software testing and are also challenging to reproduce. Therefore, automatically predicting ARBs before software release can help developers reduce ARB impact or avoid ARBs. Many bug prediction approaches have been proposed, and most of them show effectiveness in within-project prediction settings. However, due to the low presence and reproducing difficulty of ARBs, it is usually hard to collect sufficient training data to build an accurate prediction model. A recent work proposed a method named Transfer Learning based Aging-related bug Prediction (TLAP) for performing cross-project ARB prediction. Although this method considerably improves cross-project ARB prediction performance, it has been observed that its prediction result is affected by several key factors, such as the normalization methods, kernel functions, and machine learning classifiers. Therefore, this paper presents the first empirical study to examine the impact of these factors on the effectiveness of cross-project ARB prediction in terms of single-factor pattern, bigram pattern, and triplet pattern and validates the results with the Scott-Knott test technique. We find that kernel functions and classifiers are key factors affecting the effectiveness of cross-project ARB prediction, while normalization methods do not show statistical influence. In addition, the order of values in three single-factor patterns is maintained in three bigram patterns and one triplet pattern to a large extent. Similarly, the order of values in the three bigram patterns is also maintained in the triplet pattern.