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Theoretical Background and State-of-the-Art

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Mining Software Engineering Data for Software Reuse

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

This chapter provides an overview of the background knowledge that is relevant to the main areas of application of this book. The areas of software engineering, software reuse, and software quality are discussed in the context of taking advantage of useful data in order to improve the software development process. Upon providing the relevant definitions and outlining the data and metrics provided as part of software development, we discuss how data mining techniques can be applied to software engineering data and illustrate the reuse potential that is provided in an integrated manner.

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Notes

  1. 1.

    According to Sommerville [1], the term was first proposed in 1968 at a conference held by NATO. Nowadays, we may use the definition by IEEE [2], which describes software engineering as “the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software”.

  2. 2.

    In the context of this book, software reuse can be defined as “the systematic application of existing software artifacts during the process of building a new software system, or the physical incorporation of existing software artifacts in a new software system” [5]. Note that the term “artifact” corresponds to any piece of software engineering-related data and covers not only source code components, but also software requirements, documentation, etc.

  3. 3.

    A formalized alternative is that of reuse-oriented software engineering, which can be defined as defining requirements, determining whether some can be covered by reusing existing components, and finally designing and develo** the system with reuse in mind (i.e., including designing component inputs/outputs and integrating them). However, this is a development style already followed by several development teams as part of their process, regardless of its traditional or modern nature. As a result, we argue that there is no need to force development teams to use a different methodology and do not focus on the reuse-oriented perspective as a formalized way of develo** software. Instead, we present here a set of methodologies and indicate the potential from using software engineering data in order to actually incorporate reuse as a philosophy of writing software.

  4. 4.

    There are several open data repositories, one of the most prominent being the EU Open Data Portal (https://data.europa.eu/euodp/en/home), which contains more than 14000 public datasets provided by different publishers.

  5. 5.

    Note that we focus here mainly on software engineering data that are available online. There are also other resources worth mining, which form what we may call the landscape of information that a developer is faced with when joining a project, a term introduced in [15]. In specific, apart from the source code of the project, the developer has to familiarize himself/herself with the architecture of the project, the software process followed, the dependent APIs, the development environment, etc.

  6. 6.

    https://stackoverflow.com/.

  7. 7.

    The term is attributed to Jeff Atwood, the one of the two creators of Stack Overflow, the other being Joel Spolsky [19, 20].

  8. 8.

    https://stackexchange.com/sites.

  9. 9.

    https://softwareengineering.stackexchange.com/.

  10. 10.

    https://pm.stackexchange.com/.

  11. 11.

    https://sqa.stackexchange.com/.

  12. 12.

    https://www.quora.com/.

  13. 13.

    https://www.reddit.com/.

  14. 14.

    https://www.reddit.com/r/programming/.

  15. 15.

    https://www.reddit.com/r/softwaredevelopment/.

  16. 16.

    https://www.reddit.com/r/softwarearchitecture/.

  17. 17.

    https://www.codeproject.com/.

  18. 18.

    http://central.sonatype.org/.

  19. 19.

    https://www.npmjs.com/.

  20. 20.

    https://pypi.python.org/pypi.

  21. 21.

    https://search.maven.org/stats.

  22. 22.

    https://www.programmableweb.com/.

  23. 23.

    https://www.bugzilla.org/.

  24. 24.

    https://bugzilla.mozilla.org/.

  25. 25.

    https://bugs.eclipse.org/bugs/.

  26. 26.

    https://bugzilla.kernel.org/.

  27. 27.

    http://boa.cs.iastate.edu/.

  28. 28.

    http://ghtorrent.org/.

  29. 29.

    Indicatively, we may refer to an excerpt of the rather abstract “definition” provided by Robert Persig [22]: “Quality... you know what it is, yet you don’t know what it is”.

  30. 30.

    The SWEBOK, short for Software Engineering Body of Knowledge, is an international ISO standard that has been created by the cooperative efforts of software engineering professionals and is published by the IEEE Computer Society. The standard aspires to summarize the basic knowledge of software engineering and include reference lists for its different concepts.

  31. 31.

    A rather important note is that any metric should be used when it fits the context and when its value indeed is meaningful. A popular quote, often attributed to Microsoft co-founder and former chairman Bill Gates, denotes that “Measuring programming progress by lines of code is like measuring aircraft building progress by weight”.

  32. 32.

    This original definition of LCOM has been considered rather hard to compute, as its maximum value depends on the number of method pairs. Another computation formula was proposed later by Henderson-Sellers [36], usually referred as LCOM3 (and the original metric then referred as LCOM1), which defines LCOM as \((M - A / V) / (M - 1)\), where M is the number of methods and A is the number of accesses for the V class variables.

  33. 33.

    The interested reader is further referred to [39] for a comprehensive review of object-oriented static analysis metrics.

  34. 34.

    There are several other metrics in this field; for an outline of popular metrics, the reader is referred to [40].

  35. 35.

    On the other hand, static analysis is usually focused on the characteristics of maintainability and usability.

  36. 36.

    MTTF is actually the reciprocal of ROCOF.

  37. 37.

    In fact, the original publication by Albrecht [44] indicated that function-oriented and size-oriented metrics were highly correlated; however, the rise of different programming languages and models quickly proved that their differences are quite substantial [45].

  38. 38.

    There are of course other more focused interpretations, such as the one provided in [50], which defines testing as “the process of executing a program with the intent of finding errors”. As, however, times change, testing has grown to be a major process that can add actual value to the software, and thus it is not limited to error discovery. An interesting view on this matter is provided by Beizer [51], who claims that different development teams have different levels of maturity concerning the purpose of testing, with others defining it as equivalent to that of debugging, others viewing it as a way to show that the software works, others using it to reduce product quality risks, etc.

  39. 39.

    An alternative definition provided by Hand et al. [58] involves the notions of “finding unsuspected relationships” and “summarizing data in novel ways”. These phrases indicate the real challenges that differentiate data mining from traditional data analysis.

  40. 40.

    Another possible distinction is that of the type of the input data used, which, according to **e et al. [68], fall into three large categories: sequences (e.g., execution traces), graphs (e.g., call graphs), and text (e.g., documentation).

  41. 41.

    We analyze here certain popular directions; however, we obviously do not exhaust all of them. An indicative list of applications of source code analysis for the interested reader can be found at [77].

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    Themistoklis Diamantopoulos & Andreas L. Symeonidis

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Diamantopoulos, T., Symeonidis, A.L. (2020). Theoretical Background and State-of-the-Art. In: Mining Software Engineering Data for Software Reuse. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-030-30106-4_2

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