Automated Mass Maintenance of Software Assets

Veerman, Niels

doi:10.1109/csmr.2007.15

Cited by 7 publications

(6 citation statements)

References 103 publications

(239 reference statements)

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“…In other words, this research style considers industrial case studies as a vehicule for conducting research, not merely as a way to demonstrate the value of research results. We believe that this cooperation mode (1) is beneficial for both industrial and academic partners and (2) is particularly well-suited for research in software maintenance and evolution, as already experienced by other PhD researchers in the past [17], [18].…”

Section: Lessons Learnedmentioning

confidence: 94%

Program analysis and transformation for data-intensive system evolution

Cleve

2010

2010 IEEE International Conference on Software Maintenance

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Data-intensive software systems are generally made of a database and a collection of application programs in strong interaction with the former. They constitute critical assets in most enterprises, since they support business activities in all production and management domains. Data-intensive systems form most of the so-called legacy systems: they typically are one or more decades old, they are very large, heterogeneous and highly complex. Many of them significantly resist modifications and change due to the lack of documentation, to the use of aging technologies and to inflexible architectures. Therefore, the evolution of data-intensive systems clearly calls for automated support. This thesis explores the use of automated program analysis and transformation techniques in support to the evolution of the database component of the system. The program analysis techniques aim to ease the database evolution process, by helping the developers to understand the data structures that are to be changed, despite the lack of precise and up-to-date documentation. The objective of the program transformation techniques is to support the adaptation of the application programs to the new database. This adaptation process is studied in the context of two realistic database evolution scenarios, namely database database schema refactoring and database platform migration.

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Section: Lessons Learnedmentioning

confidence: 94%

Program analysis and transformation for data-intensive system evolution

Cleve

2010

2010 IEEE International Conference on Software Maintenance

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“…Although a successful product family such as described above will live for a long time, its components may live shorter lives. Components may become obsolete (unused), or their maintainability has deteriorated over the years (accumulated technical debt) such that replacement or rejuvenation 3,4 is required, or a newer, better component has arrived which can replace the "legacy" component. From the perspective of the entire product family, replacing the old component with the new component is called a "refactoring."…”

Section: Introductionmentioning

confidence: 99%

Large‐scale semi‐automated migration of legacy C/C++ test code

et al. 2022

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This is an industrial experience report on a large semi-automated migration of legacy test code in C and C++. The particular migration was enabled by automating most of the maintenance steps. Without automation this particular large-scale migration would not have been conducted, due to the risks involved in manual maintenance (risk of introducing errors, risk of unexpected rework, and loss of productivity). We describe and evaluate the method of automation we used on this real-world case. The benefits were that by automating analysis, we could make sure that we understand all the relevant details for the envisioned maintenance, without having to manually read and check our theories. Furthermore, by automating transformations we could reiterate and improve over complex and large scale source code updates, until they were "just right."The drawbacks were that, first, we have had to learn new metaprogramming skills. Second, our automation scripts are not readily reusable for other contexts; they were necessarily developed for this ad-hoc maintenance task. Our analysis shows that automated software maintenance as compared to the (hypothetical) manual alternative method seems to be better both in terms of avoiding mistakes and avoiding rework because of such mistakes. It seems that necessary and beneficial source code maintenance need not to be avoided, if software engineers are enabled to create bespoke (and ad-hoc) analysis and transformation tools to support it.

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“…The main instrument of software language engineering is on disciplined creation of new domain specific languages with emphasis on extensive automation. Practice shows that automated software maintenance, analysis, migration and renovation deliver considerable benefits in terms of costs and human effort compared to alternatives (manual changes, legacy rebuild, etc), especially on large scale [11,61,65]. However, automated methods do require special foundation for their successful usage.…”

Section: Introductionmentioning

confidence: 99%

MediaWiki Grammar Recovery

Zaytsev

2011

Preprint

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Automated Mass Maintenance of Software Assets

Cited by 7 publications

References 103 publications

Program analysis and transformation for data-intensive system evolution

Program analysis and transformation for data-intensive system evolution

Large‐scale semi‐automated migration of legacy C/C++ test code

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