@Article{eInformatica2017Art1, Title = {ECLogger: Cross-Project Catch-Block Logging Prediction Using Ensemble of Classifiers}, Author = {Sangeeta Lal and Neetu Sardana and Ashish Sureka}, Journal = {e-Informatica Software Engineering Journal}, Year = {2017}, Number = {1}, Pages = {7--38}, Volume = {11}, Abstract = { \textbf {Background:} Software developers insert log statements in the source code to record program execution information. However, optimizing the number of log statements in the source code is challenging. Machine learning based within-project logging prediction tools, proposed in previous studies, may not be suitable for new or small software projects. For such software projects, we can use cross-project logging prediction.\ \textbf {Aim:} The aim of the study presented here is to investigate cross-project logging prediction methods and techniques.\ \textbf {Method:} The proposed method is {ECLogger}, which is a novel, ensemble-based, cross-project, catch-block logging prediction model. In the research We use 9 base classifiers were used and combined using ensemble techniques. The performance of ECLogger was evaluated on on three open-source Java projects: Tomcat, CloudStack and Hadoop.\ \textbf {Results:} ECLogger\textsubscript {Bagging}, ECLogger\textsubscript {AverageVote}, and ECLogger\textsubscript {MajorityVote} show a considerable improvement in the average Logged F-measure ($LF$) on 3, 5, and 4 source$\rightarrow $target project pairs, respectively, compared to the baseline classifiers. ECLogger\textsubscript {AverageVote} performs best and shows improvements of 3.12\% (average $LF$) and 6.08\% (average $ACC$ -- Accuracy).\ \textbf {Conclusion:} The classifier based on ensemble techniques, such as bagging, average vote, and majority vote outperforms the baseline classifier. Overall, the ECLogger\textsubscript {AverageVote} model performs best. The results show that the CloudStack project is more generalizable than the other projects. }, Doi = {10.5277/e-Inf170101}, Keywords = {Classification Debugging Ensemble Logging Machine Leaning Source Code Analysis Tracing}, Url = {http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_11/eInformatica2017Art1.pdf} } @Article{eInformatica2017Art2, Title = {Experience Report: Introducing Kanban into Automotive Software Project}, Author = {Marek Majchrzak and Łukasz Stilger}, Journal = {e-Informatica Software Engineering Journal}, Year = {2017}, Number = {1}, Pages = {39--57}, Volume = {11}, Abstract = {The boundaries between traditional and agile approach methods are disappearing. A significant number of software projects require a continuous implementation of tasks without dividing them into sprints or strict project phases. Customers expect more flexibility and responsiveness from software vendors in response to the ever-changing business environment. To achieve better results in this field, Capgemini has begun using the Lean philosophy and Kanban techniques. \\The following article illustrates examples of different uses of Kanban and the main stakeholder of the process. The article presents the main advantages of transparency and ways to improve the customer co-operation as well as stakeholder relationships. The Authors try to visualise all of the elements in the context of the project. \\There is also a discussion of different approaches in two software projects. The article focuses on the main challenges and the evolutionary approach used. An attempt is made to answer the question how to convince both the team as well as the customer, and how to optimise ways to achieve great results.}, Doi = {10.5277/e-Inf170102}, Keywords = {kanban, lean, automotive, scrum, agile}, Url = {http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_11/eInformatica2017Art2.pdf} } @Article{eInformatica2017Art3, Title = {Systematic Literature Review on Search Based Mutation Testing}, Author = {Nishtha Jatana and Bharti Suri and Shweta Rani}, Journal = {e-Informatica Software Engineering Journal}, Year = {2017}, Number = {1}, Pages = {59--76}, Volume = {11}, Abstract = {Search based techniques have been widely applied in the domain of software testing. This Systematic Literature Review aims to present the research carried out in the field of search based approaches applied particularly to mutation testing. During the course of literature review, renowned databases were searched for the relevant publications in the field to include relevant studies up to the year 2014. Few studies for the year 2015--2016, gathered by performing snowball search, have also been included. For reviewing the literature in the field, 43 studies were evaluated, out of which 18 studies were thoroughly studied and analysed. The result of this SLR shows that search based techniques were applied to mutation testing primarily for two purposes, either for mutant optimisation or for test case optimisation. The future directions of this SLR suggests the application of search based techniques for other issues related to mutation testing, like, solution to equivalents mutants, generation of non-trivial mutants, multi-objective test data generation and non-functional testing.}, Doi = {10.5277/e-Inf170103}, Keywords = {software testing, analysis and verification, systematic reviews and mapping studies}, Url = {http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_11/eInformatica2017Art3.pdf} } @Article{eInformatica2017Art4, Title = {Efficiency of Software Testing Techniques: A~Controlled Experiment Replication and Network Meta-analysis}, Author = {Omar S. Gómez and Karen Cortés-Verdín and César J. Pardo}, Journal = {e-Informatica Software Engineering Journal}, Year = {2017}, Number = {1}, Pages = {77--102}, Volume = {11}, Abstract = { \textbf {Background.} Common approaches to software verification include static testing techniques, such as code reading, and dynamic testing techniques, such as black-box and white-box testing. \textbf {Objective.} With the aim of gaining a~better understanding of software testing techniques, a~controlled experiment replication and the synthesis of previous experiments which examine the efficiency of code reading, black-box and white-box testing techniques were conducted. \textbf {Method.} The replication reported here is composed of four experiments in which instrumented programs were used. Participants randomly applied one of the techniques to one of the instrumented programs. The outcomes were synthesized with seven experiments using the method of network meta-analysis (NMA). \textbf {Results.} No significant differences in the efficiency of the techniques were observed. However, it was discovered the instrumented programs had a~significant effect on the efficiency. The NMA results suggest that the black-box and white-box techniques behave alike; and the efficiency of code reading seems to be sensitive to other factors. \textbf {Conclusion.} Taking into account these findings, the Authors suggest that prior to carrying out software verification activities, software engineers should have a~clear understanding of the software product to be verified; they can apply either black-box or white-box testing techniques as they yield similar defect detection rates. }, Doi = {10.5277/e-Inf170104}, Keywords = {software verification, software testing, controlled experiment, experiment replication, meta-analysis, network meta-analysis, quantitative synthesis}, Url = {http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_11/eInformatica2017Art4.pdf} } @Article{eInformatica2017Art5, Title = {NRFixer: Sentiment Based Model for Predicting the Fixability of Non-Reproducible Bugs}, Author = {Anjali Goyal and Neetu Sardana}, Journal = {e-Informatica Software Engineering Journal}, Year = {2017}, Number = {1}, Pages = {103--116}, Volume = {11}, Abstract = {Software maintenance is an essential step in software development life cycle. Nowadays, software companies spend approximately 45\% of total cost in maintenance activities. Large software projects maintain bug repositories to collect, organize and resolve bug reports. Sometimes it is difficult to reproduce the reported bug with the information present in a bug report and thus this bug is marked with resolution non-reproducible (NR). When NR bugs are reconsidered, a few of them might get fixed (NR-to-fix) leaving the others with the same resolution (NR). To analyse the behaviour of developers towards NR-to-fix and NR bugs, the sentiment analysis of NR bug report textual contents has been conducted. The sentiment analysis of bug reports shows that NR bugs' sentiments incline towards more negativity than reproducible bugs. Also, there is a noticeable opinion drift found in the sentiments of NR-to-fix bug reports. Observations driven from this analysis were an inspiration to develop a model that can judge the fixability of NR bugs. Thus a framework, {NRFixer,} which predicts the probability of NR bug fixation, is proposed. {NRFixer} was evaluated with two dimensions. The first dimension considers meta-fields of bug reports (model-1) and the other dimension additionally incorporates the sentiments (model-2) of developers for prediction. Both models were compared using various machine learning classifiers (Zero-R, Na\"{\i }ve Bayes, J48, random tree and random forest). The bug reports of Firefox and Eclipse projects were used to test {NRFixer}. In Firefox and Eclipse projects, J48 and Na\"{\i }ve Bayes classifiers achieve the best prediction accuracy, respectively. It was observed that the inclusion of sentiments in the prediction model shows a rise in the prediction accuracy ranging from 2 to 5\% for various classifiers.}, Doi = {10.5277/e-Inf170105}, Keywords = {bug report, bug triaging, non-reproducible bugs, sentiment analysis, mining software repositories}, Url = {http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_11/eInformatica2017Art5.pdf} } @Article{eInformatica2017Art6, Title = {Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?}, Author = {Anjali Goyal and Neetu Sardana}, Journal = {e-Informatica Software Engineering Journal}, Year = {2017}, Number = {1}, Pages = {117--141}, Volume = {11}, Abstract = {Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports.}, Doi = {10.5277/e-Inf170106}, Keywords = {bug triaging, bug report assignment, developer recommendation, machine learning, information retrieval}, Url = {http://www.e-informatyka.pl/attach/e-Informatica_-_Volume_11/eInformatica2017Art6.pdf} }