{"id":507,"date":"2016-10-20T04:23:44","date_gmt":"2016-10-20T04:23:44","guid":{"rendered":"http:\/\/kusuaks7\/?p=112"},"modified":"2025-03-14T10:29:40","modified_gmt":"2025-03-14T10:29:40","slug":"data-mining-in-software-development","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/software-ux-ui\/data-mining-in-software-development\/","title":{"rendered":"Data Mine Your Way Towards Quality Software"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tThe Current Software Engineering Landscape\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tToday’s software engineering (SE) practices stretch between increasingly complex component development and the need for automation of the software development lifecycle. Indeed, on the software expert side, a plethora of new enabling technologies promises to simplify the development process; on the end user side, however, the urge for rapid development of domain-specific applications increases too fast for any new technology to cope with.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tUpon software product creation, developers typically have to identify the basic requirements, define (semi-)formal specifications, sketch a draft system architecture, and proceed with software prototyping, where they usually follow hybrid architectures, integrating open source code and\/or components from (their own) proprietary software repositories. These phases may be repeated in an iterative fashion, bringing about additional overhead. In particular,\u00a0the process of searching for the most suitable \u2013 and inter-compatible \u2013 software resources may introduce significant delays in the SE process, as reconfiguration and extensive testing is generally required.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPractically, one could benefit through the reuse of existing software solutions, both open source and proprietary ones, which have to be appropriately validated<\/strong> in order to lead to quality software<\/strong>.<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tUnderstanding Why Software Projects Fail\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nIt is common knowledge that the success or failure of a software project is related to a number of parameters that may or may not have anything to do with the approach followed or the complexity of the problem. In fact, Barry Boehm [1], in 2002, identified six top reasons for the failure of a software project \u2013\u2013\u00a0namely, incomplete requirements, lack of user involvement, lack of resources, unrealistic expectations, lack of executive support, and changes in requirements and specifications. A more recent study argues that software project complexity (and thus unpredictability) is the result of the convergence of three factors: User Requirements, Technology, and People (often referred to as the Stacey Matrix or Stacey Graph). Applied to software development, the Stacey Matrix aligns software projects into four categories according to their disposition on agreement on user requirements and certainty on technology<\/a>: Simple, Complicated, Complex, and Chaotic (Figure 1).\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFigure 1. Success and failure of software project based on methodology<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tObviously, in order to improve software project success rate, projects should avoid the chaotic classification. If the software project were\u00a0decomposed into well-designed components, complexity would decrease, leading to increased success ratio.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIntroducing Data Mining into the Picture\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTo this end, data mining could prove an appropriate path towards generating reusable, well-defined and quality-tested software artifacts, which can be integrated with minimum effort, thus leading to successful software projects. Data mining for Software Engineering can be (and has been) applied at various levels of abstraction: a) at the software model\/team management level, <\/strong>where static and dynamic mining techniques aim to localize and predict software bugs, b) at the software requirements\/software design level<\/strong>, where association analysis and clustering techniques are performed for generating recommendations, c) at the software development<\/strong>\/source code level<\/strong>, where techniques are applied in order to identify quality characteristics, as well as the reusability potential of source code, and\u00a0d) at the operation\/live-mode level, <\/strong>where outlier analysis techniques are performed for early\u00a0diagnosis and self-healing of intelligent software systems (such as autonomous robots, IoT systems, and software agents).\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\ta. Predicting Software Bugs\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tLately, several researchers have focused on Software Reliability Prediction (SRP). The problem at hand focuses on discovering defect-prone components in a software project either pre-release or post-release. Thus, the main aim is to predict whether a software source code artifact contains code that either has defects or could potentially evolve as such. The term \u201ccomponent\u201d (defined as software artifacts in a wider context) may refer to a class or a package (in an object-oriented perspective), but higher-level components or even \u201cinter-project\u201d level components (e.g. software projects or web services) are also possible. In the context of Software Reliability, current data mining approaches focus on predicting defect-prone components [2-6] using several quantitative metrics that are known to be satisfactory predictors of reliability [3].\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tb. Enhancing the Software Requirements Process\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tRequirements Engineering may refer to any process and\/or artifact related to software requirements, while the \u201cgiven context\u201d refers to the project-specific information provided by the developers and the stakeholders of a project. The need for proper identification of software requirements is crucial, since reengineering costs resulting from poorly specified requirements are considerably high [7]. In this context, and given the popularity of open source software initiatives and the adoption of diverse methodologies, several researchers have proposed data mining techniques and recommendation systems technologies [8]. This way they aspire to identify the features of a system [9], non-functional characteristics [10], and even the relation between requirements and stakeholders [11].\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tc. Mining Source Code\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tGiven the flourishing of the open-source software paradigm, mining for information from source code repositories has also been very popular. Various approaches exist; some of them aim to improve auto-correction [12] or find syntax errors [13] in order to assist software developers when writing code. Other approaches aspire to identify the structural patterns of a programming language through Abstract Syntax Trees [14] and N-gram language models [15] extraction, thus leading to conclusions with respect to the quality characteristics of software. Mining code snippets [16], in order to identify similar code patterns, and APIs are also extremely popular in Code Search Engines, while frequent tree mining algorithms have also been used extensively [17], aspiring to identify common behavior patterns, focusing on the reuse perspective of software components.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\td. Identifying Faulty Behaviors of Intelligent Software\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAutonomous software systems have to operate following an open-world assumption paradigm and have to advance their intelligence mechanism to make decisions and act\/react appropriately. Robustness is critical, so failure detection and handling, and respective adaptation of behavior based on system health and fault detection are essential [18]. Fault diagnosis consists of detecting anomalies in the behavior of systems and subsystems. Early fault detection can improve the performance of software systems [19] and can also help during testing and debugging. Statistical methods, such as particle filters, neural networks and Bayesian methods have been used [20] to help with fault detection and identification. Lately, other machine learning techniques, namely GMMs [21] and one-class SVMs [22], seem to be a better fit\u00a0in reducing software \u201cfailures\u201d to a minimum, thus requiring as little human intervention as possible.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nEach one of these categories will be thoroughly discussed in the series of blogs to come, where emphasis will be placed on\u00a0the advantages of applying data mining in the various software engineering facets.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tReferences\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[1] \u00a0\u00a0\u00a0 Boehm, B.: Software engineering is a value-based contact sport. Software, IEEE 19.5: 95-96 (2002).<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[2] \u00a0\u00a0\u00a0 Diamantopoulos T., and Symeonidis, A.L., Towards Interpretable Defect-Prone Component Analysis using Genetic Fuzzy Systems. In Proceedings of the IEEE\/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE’15), pp.32-38, Florence, Italy, 2015.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[3] \u00a0\u00a0\u00a0 Marco D\u2019Ambros, Michele Lanza, and Romain Robbes. Evaluating Defect Prediction Approaches: A Benchmark and an Extensive Comparison. Empirical Software Engineering, 17(4\u20135):531\u2013577, August 2012.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[4]\u00a0\u00a0\u00a0\u00a0 S. R. Chidamber and C. F. Kemerer. A Metrics Suite for Object Oriented Design. IEEE Transactions on Software Engineering, 20(6):476\u2013493, June 1994.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[5] \u00a0\u00a0\u00a0 F. Brito e Abreu and W. Melo. Evaluating the Impact of Object-Oriented Design on Software Quality. In Proceedings of the 3rd International Software Metrics Symposium, pages 90\u201399, Mar 1996.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[6] \u00a0\u00a0\u00a0 Raimund Moser, Witold Pedrycz, and Giancarlo Succi. A Comparative Analysis of the Efficiency of Change Metrics and Static Code Attributes for Defect Prediction. In Proceedings of the 30th International Conference on Software Engineering, pages 181\u2013190, New York, USA, 2008.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[7] \u00a0\u00a0\u00a0 Dean Leffingwell. Calculating your return on investment from more effective requirements management. American Programmer, 10(4):13\u2013 16, 1997.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[8]\u00a0\u00a0\u00a0\u00a0 A. Felfernig, G. Ninaus, H. Grabner, F. Reinfrank, L. Weninger , D. Pagano, and W. Maalej. An overview of recommender systems in requirements engineering. In Walid Maalej and Anil Kumar Thurimella, editors, Managing Requirements Knowledge, pages 315\u2013332. Springer Berlin Heidelberg, 2013.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[9] \u00a0\u00a0\u00a0 Vander Alves, Christa Schwanninger, Luciano Barbosa, Awais Rashid, Peter Sawyer, Paul Rayson, Christoph Pohl, and Andreas Rummler. An exploratory study of information retrieval techniques in domain analysis. In Proceedings of the 2008 12th International Software Product Line Conference, SPLC \u201908, pages 67\u201376,Washington, DC, USA, 2008. IEEE Computer Society.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t[10] \u00a0 Jose Romero-Mariona, Hadar Ziv, and Debra J. Richardson. Srrs: A recommendation system for security requirements. In Proceedings of the 2008 International Workshop on Recommendation Systems for Software Engineering, RSSE \u201908, pages 50\u201352, New York, NY, USA, 2008. ACM.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\n[11] \u00a0 Bamshad Mobasher and Jane Cleland-Huang. Recommender systems in requirements engineering. The AI magazine, 32(3):81\u201389, 2011.<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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How you can use Data Mining techniques to enhance your software development process and the various advantages of this approach.<\/p>\n","protected":false},"author":294,"featured_media":2862,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[200],"tags":[106],"ppma_author":[1612],"class_list":["post-507","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-ux-ui","tag-software"],"authors":[{"term_id":1612,"user_id":294,"is_guest":0,"slug":"andreas-symeonidis","display_name":"Andreas Symeonidis","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Symeonidis","first_name":"Andreas","job_title":"","description":"Andreas is an Assistant Professor with the Department of Electrical and Computer Engineering at the Aristotle University of Thessaloniki. He holds a PhD in Electrical and Computer Engineering and did his postdoctoral research on the Evaluation of Agent Efficiency. His research interests include Agent-oriented Software Engineering (AOSE) and Software Engineering (SE) processes, data mining and knowledge extraction, intelligent systems, social networks and evolutionary computing. "}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/507","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/294"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=507"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/507\/revisions"}],"predecessor-version":[{"id":37411,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/507\/revisions\/37411"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2862"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=507"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=507"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=507"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=507"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}