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Relevant bibliographies by topics / Mining software engineering data

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Academic literature on the topic 'Mining software engineering data'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Mining software engineering data"

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Halkidi, M., D. Spinellis, G. Tsatsaronis, and M. Vazirgiannis. "Data mining in software engineering." Intelligent Data Analysis 15, no. 3 (May 4, 2011): 413–41. http://dx.doi.org/10.3233/ida-2010-0475.

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Xie, Tao, Suresh Thummalapenta, David Lo, and Chao Liu. "Data Mining for Software Engineering." Computer 42, no. 7 (August 2009): 55–62. http://dx.doi.org/10.1109/mc.2009.256.

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Hall, Robert J. "Editorial: data mining in software engineering." Automated Software Engineering 17, no. 4 (July 13, 2010): 373–74. http://dx.doi.org/10.1007/s10515-010-0073-9.

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Marbán, Oscar, Javier Segovia, Ernestina Menasalvas, and Covadonga Fernández-Baizán. "Toward data mining engineering: A software engineering approach." Information Systems 34, no. 1 (March 2009): 87–107. http://dx.doi.org/10.1016/j.is.2008.04.003.

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Taylor, Quinn, Christophe Giraud Carrier, and Charles D. Knutson. "Applications of data mining in software engineering." International Journal of Data Analysis Techniques and Strategies 2, no. 3 (2010): 243. http://dx.doi.org/10.1504/ijdats.2010.034058.

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Periasamy, A. R. Pon, and A. Mishbahulhuda. "Applications of Data Mining Techniques in Software Engineering." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 3 (March 30, 2017): 304–7. http://dx.doi.org/10.23956/ijarcsse/v7i3/0174.

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Canaparo, Marco, and Elisabetta Ronchieri. "Data Mining Techniques for Software Quality Prediction in Open Source Software." EPJ Web of Conferences 214 (2019): 05007. http://dx.doi.org/10.1051/epjconf/201921405007.

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Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models.

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KAJKO-MATTSSON, MIRA, and NED CHAPIN. "DATA MINING FOR VALIDATION IN SOFTWARE ENGINEERING: AN EXAMPLE." International Journal of Software Engineering and Knowledge Engineering 14, no. 04 (August 2004): 407–27. http://dx.doi.org/10.1142/s0218194004001725.

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Consider two independently done software engineering studies that used different approaches to cover some of the same subject area, such as software maintenance. Although done differently and for different purposes, to what extent can each study serve as a validation of the other? Within the scope of the subject area overlap, data mining can be applied to provide a quantitative assessment. This paper reports on the data mining that attempted to cross validate two independently done and published software engineering studies of software maintenance, one on a corrective maintenance maturity model, and the other on an objective classification of software maintenance activities. The data mining established that each of the two independently done studies effectively and very strongly validates the other.

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KHOSHGOFTAAR, TAGHI M., EDWARD B. ALLEN, WENDELL D. JONES, and JOHN P. HUDEPOHL. "DATA MINING FOR PREDICTORS OF SOFTWARE QUALITY." International Journal of Software Engineering and Knowledge Engineering 09, no. 05 (October 1999): 547–63. http://dx.doi.org/10.1142/s0218194099000309.

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"Knowledge discovery in data bases" (KDD) for software engineering is a process for finding useful information in the large volumes of data that are a byproduct of software development, such as data bases for configuration management and for problem reporting. This paper presents guidelines for extracting innovative process metrics from these commonly available data bases. This paper also adapts the Classification And Regression Trees algorithm, CART, to the KDD process for software engineering data. To our knowledge, this algorithm has not been used previously for empirical software quality modeling. In particular, we present an innovative way to control the balance between misclassification rates. A KDD case study of a very large legacy telecommunications software system found that variables derived from source code, configuration management transactions, and problem reporting transactions can be useful predictors of software quality. The KDD process discovered that for this software development environment, out of forty software attributes, only a few of the predictor variables were significant. This resulted in a model that predicts whether modules are likely to have faults discovered by customers. Software developers need such predictions early in development to target software enhancement techniques to the modules that need improvement the most.

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Minku, Leandro L., Emilia Mendes, and Burak Turhan. "Data mining for software engineering and humans in the loop." Progress in Artificial Intelligence 5, no. 4 (April 16, 2016): 307–14. http://dx.doi.org/10.1007/s13748-016-0092-2.

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Dissertations / Theses on the topic "Mining software engineering data"

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Delorey, Daniel Pierce. "Observational Studies of Software Engineering Using Data from Software Repositories." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1716.pdf.

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Unterkalmsteiner, Michael. "Coordinating requirements engineering and software testing." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-663.

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The development of large, software-intensive systems is a complex undertaking that is generally tackled by a divide and conquer strategy. Organizations face thereby the challenge of coordinating the resources which enable the individual aspects of software development, commonly solved by adopting a particular process model. The alignment between requirements engineering (RE) and software testing (ST) activities is of particular interest as those two aspects are intrinsically connected: requirements are an expression of user/customer needs while testing increases the likelihood that those needs are actually satisfied. The work in this thesis is driven by empirical problem identification, analysis and solution development towards two main objectives. The first is to develop an understanding of RE and ST alignment challenges and characteristics. Building this foundation is a necessary step that facilitates the second objective, the development of solutions relevant and scalable to industry practice that improve REST alignment. The research methods employed to work towards these objectives are primarily empirical. Case study research is used to elicit data from practitioners while technical action research and field experiments are conducted to validate the developed solutions in practice. This thesis contains four main contributions: (1) An in-depth study on REST alignment challenges and practices encountered in industry. (2) A conceptual framework in the form of a taxonomy providing constructs that further our understanding of REST alignment. The taxonomy is operationalized in an assessment framework, REST-bench (3), that was designed to be lightweight and can be applied as a postmortem in closing development projects. (4) An extensive investigation into the potential of information retrieval techniques to improve test coverage, a common REST alignment challenge, resulting in a solution prototype, risk-based testing supported by topic models (RiTTM). REST-bench has been validated in five cases and has shown to be efficient and effective in identifying improvement opportunities in the coordination of RE and ST. Most of the concepts operationalized from the REST taxonomy were found to be useful, validating the conceptual framework. RiTTM, on the other hand, was validated in a single case experiment where it has shown great potential, in particular by identifying test cases that were originally overlooked by expert test engineers, improving effectively test coverage.

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Santamaría, Diego, and Álvaro de Ramón. "Data Mining Web-Tool Prototype Using Monte Carlo Simulations." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3164.

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Facilitating the decision making process using models and patterns is viewed in this thesis to be really helpful. Data mining is one option to accomplish this task. Data mining algorithms can show all the relations within given data, find rules and create behavior patterns. In this thesis seven different types of data mining algorithms are employed. Monte Carlo is a statistical method that is used in the developed prototype to obtain random data and to simulate different scenarios. Monte Carlo methods are useful for modeling phenomena with significant uncertainty in the inputs. This thesis presents the steps followed during the development of a web-tool prototype that uses data mining techniques to assist decision-makers of port planning to make better forecasts using generated data from the Monte Carlo simulation. The prototype generates random port planning forecasts using Monte Carlo simulation. These forecasts are then evaluated with several data mining algorithms. Then decision-makers can evaluate the outcomes of the prototype (rules, decision tress and regressions) to be able to make better decisions.

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Waters, Robert Lee. "Obtaining Architectural Descriptions from Legacy Systems: The Architectural Synthesis Process (ASP)." Diss., Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-10272004-160115/unrestricted/waters%5Frobert%5Fl%5F200412%5Fphd.pdf.

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Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2005.
Rick Kazman, Committee Member ; Colin Potts, Committee Member ; Mike McCracken, Committee Member ; Gregory Abowd, Committee Chair ; Spencer Rugaber, Committee Member. Includes bibliographical references.

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Matyja, Dariusz. "Applications of data mining algorithms to analysis of medical data." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4253.

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Medical datasets have reached enormous capacities. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. The data which is analyzed comes from the Polish National Breast Cancer Prevention Program ran in Poland in 2006. The aim of this master's thesis is the evaluation of the analytical data from the Program to see if the domain can be a subject to data mining. The next step is to evaluate several data mining methods with respect to their applicability to the given data. This is to show which of the techniques are particularly usable for the given dataset. Finally, the research aims at extracting some tangible medical knowledge from the set. The research utilizes a data warehouse to store the data. The data is assessed via the ETL process. The performance of the data mining models is measured with the use of the lift charts and confusion (classification) matrices. The medical knowledge is extracted based on the indications of the majority of the models. The experiments are conducted in the Microsoft SQL Server 2005. The results of the analyses have shown that the Program did not deliver good-quality data. A lot of missing values and various discrepancies make it especially difficult to build good models and draw any medical conclusions. It is very hard to unequivocally decide which is particularly suitable for the given data. It is advisable to test a set of methods prior to their application in real systems. The data mining models were not unanimous about patterns in the data. Thus the medical knowledge is not certain and requires verification from the medical people. However, most of the models strongly associated patient's age, tissue type, hormonal therapies and disease in family with the malignancy of cancers. The next step of the research is to present the findings to the medical people for verification. In the future the outcomes may constitute a good background for development of a Medical Decision Support System.

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Imam, Ayad Tareq. "Relative-fuzzy : a novel approach for handling complex ambiguity for software engineering of data mining models." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/3909.

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There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data.

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Thun, Julia, and Rebin Kadouri. "Automating debugging through data mining." Thesis, KTH, Data- och elektroteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-203244.

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Contemporary technological systems generate massive quantities of log messages. These messages can be stored, searched and visualized efficiently using log management and analysis tools. The analysis of log messages offer insights into system behavior such as performance, server status and execution faults in web applications. iStone AB wants to explore the possibility to automate their debugging process. Since iStone does most parts of their debugging manually, it takes time to find errors within the system. The aim was therefore to find different solutions to reduce the time it takes to debug. An analysis of log messages within access – and console logs were made, so that the most appropriate data mining techniques for iStone’s system would be chosen. Data mining algorithms and log management and analysis tools were compared. The result of the comparisons showed that the ELK Stack as well as a mixture between Eclat and a hybrid algorithm (Eclat and Apriori) were the most appropriate choices. To demonstrate their feasibility, the ELK Stack and Eclat were implemented. The produced results show that data mining and the use of a platform for log analysis can facilitate and reduce the time it takes to debug.
Dagens system genererar stora mängder av loggmeddelanden. Dessa meddelanden kan effektivt lagras, sökas och visualiseras genom att använda sig av logghanteringsverktyg. Analys av loggmeddelanden ger insikt i systemets beteende såsom prestanda, serverstatus och exekveringsfel som kan uppkomma i webbapplikationer. iStone AB vill undersöka möjligheten att automatisera felsökning. Eftersom iStone till mestadels utför deras felsökning manuellt så tar det tid att hitta fel inom systemet. Syftet var att därför att finna olika lösningar som reducerar tiden det tar att felsöka. En analys av loggmeddelanden inom access – och konsolloggar utfördes för att välja de mest lämpade data mining tekniker för iStone’s system. Data mining algoritmer och logghanteringsverktyg jämfördes. Resultatet av jämförelserna visade att ELK Stacken samt en blandning av Eclat och en hybrid algoritm (Eclat och Apriori) var de lämpligaste valen. För att visa att så är fallet så implementerades ELK Stacken och Eclat. De framställda resultaten visar att data mining och användning av en plattform för logganalys kan underlätta och minska den tid det tar för att felsöka.

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Sobolewska, Katarzyna-Ewa. "Web links utility assessment using data mining techniques." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2936.

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This paper is focusing on the data mining solutions for the WWW, specifically how it can be used for the hyperlinks evaluation. We are focusing on the hyperlinks used in the web sites systems and on the problem which consider evaluation of its utility. Since hyperlinks reflect relation to other webpage one can expect that there exist way to verify if users follow desired navigation paths. The Challenge is to use available techniques to discover usage behavior patterns and interpret them. We have evaluated hyperlinks of the selected pages from www.bth.se web site. By using web expert’s help the usefulness of the data mining as the assessment basis was validated. The outcome of the research shows that data mining gives decision support for the changes in the web site navigational structure.
akasha.kate@gmail.com

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Saltin, Joakim. "Interactive visualization of financial data : Development of a visual data mining tool." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181225.

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In this project, a prototype visual data mining tool was developed, allowing users to interactively investigate large multi-dimensional datasets visually (using 2D visualization techniques) using so called drill-down, roll-up and slicing operations. The project included all steps of the development, from writing specifications and designing the program to implementing and evaluating it. Using ideas from data warehousing, custom methods for storing pre-computed aggregations of data (commonly referred to as materialized views) and retrieving data from these were developed and implemented in order to achieve higher performance on large datasets. View materialization enables the program to easily fetch or calculate a view using other views, something which can yield significant performance gains if view sizes are much smaller than the underlying raw dataset. The choice of which views to materialize was done in an automated manner using a well-known algorithm - the greedy algorithm for view materialization - which selects the fraction of all possible views that is likely (but not guaranteed) to yield the best performance gain. The use of materialized views was shown to have good potential to increase performance for large datasets, with an average speedup (compared to on-the-fly queries) between 20 and 70 for a test dataset containing 500~000 rows. The end result was a program combining flexibility with good performance, which was also reflected by good scores in a user-acceptance test, with participants from the company where this project was carried out.

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Allahyari, Hiva. "On the concept of Understandability as a Property of Data mining Quality." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6134.

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This paper reviews methods for evaluating and analyzing the comprehensibility and understandability of models generated from data in the context of data mining and knowledge discovery. The motivation for this study is the fact that the majority of previous work has focused on increasing the accuracy of models, ignoring user-oriented properties such as comprehensibility and understandability. Approaches for analyzing the understandability of data mining models have been discussed on two different levels: one is regarding the type of the models’ presentation and the other is considering the structure of the models. In this study, we present a summary of existing assumptions regarding both approaches followed by an empirical work to examine the understandability from the user’s point of view through a survey. From the results of the survey, we obtain that models represented as decision trees are more understandable than models represented as decision rules. Using the survey results regarding understandability of a number of models in conjunction with quantitative measurements of the complexity of the models, we are able to establish correlation between complexity and understandability of the models.

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Books on the topic "Mining software engineering data"

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Diamantopoulos, Themistoklis, and Andreas L. Symeonidis. Mining Software Engineering Data for Software Reuse. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30106-4.

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Xanthopoulos, Petros. Robust Data Mining. New York, NY: Springer New York, 2013.

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Sammarco, John J. Computer-aided software engineering (CASE) for software automation. [Washington, D.C.]: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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Mining software specifications: Methodologies and applications. Boca Raton, FL: CRC Press, 2011.

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1962-, Liu Jiming, Cheung Yiuming 1971-, and Yin Hujun 1962-, eds. Intelligent data engineering and automated learning: Revised papers. Berlin: Springer, 2003.

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Emilio, Corchado, Yin Hujun 1962-, Botti Vicente, and Fyfe Colin, eds. Intelligent data engineering and automated learning - IDEAL 2006: Data mining, financial engineering, and intelligent agents : 7th international conference, Burgos, Spain, September 20-23, 2006 : proceedings. Berlin: Springer, 2006.

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1962-, Yin Hujun, ed. Intelligent data engineering and automated learning-IDEAL 2002: Third international conference, Manchester, UK, August 2002 : proceedings. Berlin: Springer, 2002.

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Wyld, David C. Advances in Computer Science, Engineering & Applications: Proceedings of the Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), May 25-27, 2012, New Delhi, India, Volume 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Buchwald, Hagen. S-BPM ONE – Setting the Stage for Subject-Oriented Business Process Management: First International Workshop, Karlsruhe, Germany, October 22, 2009. Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Heidelberg, 2010.

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Gibbs, Betty. Public domain software for earth scientists: Handbook of public domain and inexpensive software. 2nd ed. Boulder: Gibbs Associates, 1991.

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Book chapters on the topic "Mining software engineering data"

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Menzies, Tim. "Data Mining." In Recommendation Systems in Software Engineering, 39–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45135-5_3.

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Herzig, Kim, and Andreas Zeller. "Mining Bug Data." In Recommendation Systems in Software Engineering, 131–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45135-5_6.

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Shirabad, Jelber Sayyad. "Predictive Techniques in Software Engineering." In Encyclopedia of Machine Learning and Data Mining, 992–1000. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_661.

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Anton, Carmen, Oliviu Matei, and Anca Avram. "Use of Multiple Data Sources in Collaborative Data Mining." In Intelligent Systems Applications in Software Engineering, 189–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30329-7_18.

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Chen, Jing. "Application of Data Mining Technology in Software Engineering." In Application of Intelligent Systems in Multi-modal Information Analytics, 346–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74814-2_49.

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Santos, Jaime, and Orlando Belo. "Estimating Risk Management in Software Engineering Projects." In Advances in Data Mining. Applications and Theoretical Aspects, 85–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39736-3_7.

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Shishlenin, Sergey, and Gongzhu Hu. "Predicting Access to Healthcare Using Data Mining Techniques." In Software Engineering Research, Management and Applications, 191–204. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11265-7_15.

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Baby Rani, A. S., and A. R. Nadira Banu Kamal. "Automatically Labeling Software Components with Concept Mining." In Emerging Research in Data Engineering Systems and Computer Communications, 473–86. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0135-7_44.

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van der Aalst, Wil M. P. "Object-Centric Process Mining: Dealing with Divergence and Convergence in Event Data." In Software Engineering and Formal Methods, 3–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30446-1_1.

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Neruda, Roman. "Towards Data-Driven Hybrid Composition of Data Mining Multi-agent Systems." In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 271–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01203-7_24.

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Conference papers on the topic "Mining software engineering data"

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Xie, Tao, Jian Pei, and Ahmed E. Hassan. "Mining Software Engineering Data." In 29th International Conference on Software Engineering (ICSE'07 Companion). IEEE, 2007. http://dx.doi.org/10.1109/icsecompanion.2007.50.

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Hassan, Ahmed E., and Tao Xie. "Mining software engineering data." In the 32nd ACM/IEEE International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1810295.1810451.

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Turhan, Burak, and Onur Kutlubay. "Mining Software Data." In 2007 IEEE 23rd International Conference on Data Engineering Workshop. IEEE, 2007. http://dx.doi.org/10.1109/icdew.2007.4401084.

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Gousios, Georgios, and Diomidis Spinellis. "Mining Software Engineering Data from GitHub." In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 2017. http://dx.doi.org/10.1109/icse-c.2017.164.

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Saeed, Sabeer, Mohammed Mansur Abubakar, and Murat Karabatak. "Software Engineering for Data Mining (ML-Enabled) Software Applications." In 2021 9th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2021. http://dx.doi.org/10.1109/isdfs52919.2021.9486319.

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El-Ramly, M. "Mining software usage data." In "International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering. IEE, 2004. http://dx.doi.org/10.1049/ic:20040478.

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Menzies, Tim. "Beyond data mining; towards "idea engineering"." In PROMISE '13: 9th International Conference on Predictive Models in Software Engineering. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2499393.2499401.

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Deng, Fengxian. "Research Progress on Software Engineering Data Mining Technology." In 2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/etmhs-15.2015.129.

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Ma, Jie. "Application of Data Mining Technology in Software Engineering." In 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/msmee-17.2017.35.

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Wang, Hongpo, Linnan Bai, Ming Jiezhang, Jun Zhang, and Qiang Li. "Software Testing Data Analysis Based on Data Mining." In 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017. http://dx.doi.org/10.1109/icisce.2017.148.

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Reports on the topic "Mining software engineering data"

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Woyna, M. A., and C. R. Carlson. Evaluation of computer-aided software engineering tools for data base development. Office of Scientific and Technical Information (OSTI), February 1989. http://dx.doi.org/10.2172/5698662.

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Grossman, R. The High Performance and Wide Area Analysis and Mining of Scientific & Engineering Data. Office of Scientific and Technical Information (OSTI), December 2002. http://dx.doi.org/10.2172/836590.

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Dempsey, Terri L. Handling the Qualitative Side of Mixed Methods Research: A Multisite, Team-Based High School Education Evaluation Study. RTI Press, September 2018. http://dx.doi.org/10.3768/rtipress.2018.mr.0039.1809.

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Abstract:

Attention to mixed methods studies research has increased in recent years, particularly among funding agencies that increasingly require a mixed methods approach for program evaluation. At the same time, researchers operating within large-scale, rapid-turnaround research projects are faced with the reality that collection and analysis of large amounts of qualitative data typically require an intense amount of project resources and time. However, practical examples of efficiently collecting and handling high-quality qualitative data within these studies are limited. More examples are also needed of procedures for integrating the qualitative and quantitative strands of a study from design to interpretation in ways that can facilitate efficiencies. This paper provides a detailed description of the strategies used to collect and analyze qualitative data in what the research team believed to be an efficient, high-quality way within a team-based mixed methods evaluation study of science, technology, engineering, and math (STEM) high-school education. The research team employed an iterative approach to qualitative data analysis that combined matrix analyses with Microsoft Excel and the qualitative data analysis software program ATLAS.ti. This approach yielded a number of practical benefits. Selected preliminary results illustrate how this approach can simplify analysis and facilitate data integration.

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