Splitting a Large Software Archive for Easing Future Software Evolution - An Industrial Experience Report using Formal Concept Analysis

Glorie, M.; Zaidman, Andy; Hofland, Lennart; Deursen, Arie van

doi:10.1109/csmr.2008.4493310

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…This medical imaging product has a multi-MLOC software repository, called an archive in Philips Medical System's terminology [1]. The repository contains approximately 30,000 source code files that are being developed (and maintained) using branching.…”

Section: Introductionmentioning

confidence: 99%

Splitting a large software repository for easing future software evolution—an industrial experience report

Glorie

Zaidman

Deursen

et al. 2009

J. Softw. Maint. Evol.: Res. Pract.

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SUMMARYPhilips Medical Systems produces medical diagnostic imaging products, such as MR, X-ray and CT systems. The software of these devices is complex, has been evolving for several decades and is currently a multi-MLOC monolithic software repository. In this paper we report on splitting a single software repository into multiple smaller repositories so that these can be developed independently, easing the software's evolution. For splitting the single software repository, we set up two experiments that involve well-known analysis techniques, namely formal concept analysis and clustering. Because of the sheer size of the monolithic software repository, we also propose to use a 'leveled approach', which implies that the analysis technique is applied in several iterations, whereby in some iterations only part of the application is subjected to the analysis technique. Unfortunately, both analysis techniques failed to produce an acceptable partitioning of the monolithic software repository, even if they are combined with our newly proposed leveled approach. We provide a number of valuable lessons learned, which might prevent others from falling into the same pitfalls.

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Section: Introductionmentioning

confidence: 99%

Splitting a large software repository for easing future software evolution—an industrial experience report

Glorie

Zaidman

Deursen

et al. 2009

J. Softw. Maint. Evol.: Res. Pract.

Self Cite

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“…These binary features are derived from data flow and control flow information, resulting in features such as uses global variable x or calls function f. Two techniques are explained to calculate this maximal association: formal concept analysis and (maximal) frequent itemset mining. While both techniques conceptually do the same, it has been shown that concept analysis approach does not always scale in a reengineering context [6]. This is why we will first try the mining approach.…”

Section: Figure 2 Functional Dependency View Of Industrial Casementioning

confidence: 99%

“…al. [6] also used information extracted from project documentation in an attempt to split a large software archive with formal concept analysis. Similarly, we could try to extract business information from project documentation.…”

Section: Getting To Businessmentioning

confidence: 99%

Reverse Engineering in the World of Enterprise SOA

Geet

2008

2008 15th Working Conference on Reverse Engineering

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With IT turning into a commodity, it is losing its ability to create competitive advantage. Therefore, enterprises should spend more time obtaining a reliable and costeffective IT infrastructure. While Service Oriented Architectures can help cope with this mindshift, the problem of integrating legacy into a new environment and aligning it with current business needs remains. In my PhD, I research how we can leverage existing reverse engineering techniques in an industrial context to aid in understanding existing legacy and prepare a migration towards a Service Oriented Architecture.

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“…The technique was applied to 10 public domain and 10 industrial programs. Glorie et al [25] reported their experiences in applying formal concept analysis (FCA) on a system comprised of 30 KLOC. They discussed how FCA can be applied to non-trivial software by using a real-world system, presented the leveled approach (a technique to cope with scalability issues using FCA), and a measure of concept quality (to choose the optimal concepts from a set of candidate concepts).…”

Section: Software Module Clusteringmentioning

confidence: 99%