5,534 research outputs found
Towards Automated Performance Bug Identification in Python
Context: Software performance is a critical non-functional requirement,
appearing in many fields such as mission critical applications, financial, and
real time systems. In this work we focused on early detection of performance
bugs; our software under study was a real time system used in the
advertisement/marketing domain.
Goal: Find a simple and easy to implement solution, predicting performance
bugs.
Method: We built several models using four machine learning methods, commonly
used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian
Networks, and Logistic Regression.
Results: Our empirical results show that a C4.5 model, using lines of code
changed, file's age and size as explanatory variables, can be used to predict
performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that
reducing the number of changes delivered on a commit, can decrease the chance
of performance bug injection.
Conclusions: We believe that our approach can help practitioners to eliminate
performance bugs early in the development cycle. Our results are also of
interest to theoreticians, establishing a link between functional bugs and
(non-functional) performance bugs, and explicitly showing that attributes used
for prediction of functional bugs can be used for prediction of performance
bugs
Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)
We report and fix an important systematic error in prior studies that ranked
classifiers for software analytics. Those studies did not (a) assess
classifiers on multiple criteria and they did not (b) study how variations in
the data affect the results. Hence, this paper applies (a) multi-criteria tests
while (b) fixing the weaker regions of the training data (using SMOTUNED, which
is a self-tuning version of SMOTE). This approach leads to dramatically large
increases in software defect predictions. When applied in a 5*5
cross-validation study for 3,681 JAVA classes (containing over a million lines
of code) from open source systems, SMOTUNED increased AUC and recall by 60% and
20% respectively. These improvements are independent of the classifier used to
predict for quality. Same kind of pattern (improvement) was observed when a
comparative analysis of SMOTE and SMOTUNED was done against the most recent
class imbalance technique. In conclusion, for software analytic tasks like
defect prediction, (1) data pre-processing can be more important than
classifier choice, (2) ranking studies are incomplete without such
pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of
Software Engineering (ICSE), 201
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