36,191 research outputs found
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
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Data sets and data quality in software engineering
OBJECTIVE - to assess the extent and types of techniques used to manage quality within software engineering data sets. We consider this a particularly interesting question in the context of initiatives to promote sharing and secondary analysis of data sets.
METHOD - we perform a systematic review of available empirical software engineering studies.
RESULTS - only 23 out of the many hundreds of studies assessed, explicitly considered data quality.
CONCLUSIONS - first, the community needs to consider the quality and appropriateness of the data set being utilised; not all data sets are equal. Second, we need more research into means of identifying, and ideally repairing, noisy cases. Third, it should become routine to use sensitivity analysis to assess conclusion stability with respect to the assumptions that must be made concerning noise levels
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
What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing
Driven by new software development processes and testing in clouds, system
and integration testing nowadays tends to produce enormous number of alarms.
Such test alarms lay an almost unbearable burden on software testing engineers
who have to manually analyze the causes of these alarms. The causes are
critical because they decide which stakeholders are responsible to fix the bugs
detected during the testing. In this paper, we present a novel approach that
aims to relieve the burden by automating the procedure. Our approach, called
Cause Analysis Model, exploits information retrieval techniques to efficiently
infer test alarm causes based on test logs. We have developed a prototype and
evaluated our tool on two industrial datasets with more than 14,000 test
alarms. Experiments on the two datasets show that our tool achieves an accuracy
of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by
up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per
cause analysis. Due to the attractive experimental results, our industrial
partner, a leading information and communication technology company in the
world, has deployed the tool and it achieves an average accuracy of 72% after
two months of running, nearly three times more accurate than a previous
strategy based on regular expressions.Comment: 12 page
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