Redocumentation of a legacy banking system

Geet, Joris Van; Ebraert, Peter; Demeyer, Serge

doi:10.1145/1862372.1862382

Cited by 11 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…That means, this representation gives high weight to any term that occurs often in a particular document (rescaling on information content). (iii) More-than-one-word (n-gram) vectors provide statistics about some ordered word combinations, that is, they consider sequences of terms that appear next to each other, for example, ngram_range= (1,3). (iv) NLP based features support intelligent analysis of text, for example, by counting the number of words, characters, nouns, verbs, or adjectives.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…Software redocumentation with the intent to recover lost or nonexisting documentation is performed for various reasons and is becoming more and more important. Redocumentation may help improving software quality during maintenance 1 or supports reengineering and migration activities. 2 In the banking domain, as in our case, redocumentation projects are also initiated because of company policies or requirements of national financial supervisory authorities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

et al. 2020

View full text Add to dashboard Cite

Source code comments contain key information about the underlying software system. Many redocumentation approaches, however, cannot exploit this valuable source of information. This is mainly due to the fact that not all comments have the same goals and target audience and can therefore only be used selectively for redocumentation. Performing a required classification manually, for example, in the form of heuristics, is usually time‐consuming and error‐prone and strongly dependent on programming languages and guidelines of concrete software systems. By leveraging machine learning (ML), it should be possible to classify comments and thus transfer valuable information from the source code into documentation with less effort but the same quality. We applied classical ML techniques but also deep learning (DL) approaches to legacy systems by transferring source code comments into meaningful representations using, for example, word embeddings but also novel approaches using quick response codes or a special character‐to‐image encoding. The results were compared with industry‐strength heuristic classification. As a result, we found that ML outperforms the heuristics in number of errors and less effort, that is, we finally achieve an accuracy of more than 95% for an image‐based DL network and even over 96% for a traditional approach using a random forest classifier.

show abstract

Section: Feature Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Software engineering approaches contribute to automate (i) AI-based system testing, e.g., by means of predicting fault-prone parts of the software system that need particular attention [68], and (ii) system documentation to improve software maintainability [98,69,14] and to support re-engineering and migration activities [14]. In particular, we developed a feed-back directed testing approach to derive tests from interacting with a running system [61], which we successfully applied in various industry projects [82,24].…”

Section: Approach 5: Software Quality By Code Analysis and Automated ...mentioning

confidence: 99%

Applying AI in Practice: Key Challenges and Lessons Learned

Fischer¹,

Ehrlinger²,

Geist³

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment.

show abstract

“…ii) Documentación: La ingeniería inversa se convierte en la principal estrategia para reconstruir la documentación de un sistema software, al lograr una representación de más alto nivel que permita su comprensión al recuperar las vistas de diseño y arquitectura, los requerimientos e incluso los modelos del negocio (Van Geet et al, 2010). También se puede utilizar para mantener el control de versiones en sistemas legados a partir de sus implementaciones, porque facilita la recuperación de artefactos en sistemas que han sido desarrollados hace mucho tiempo y que son difíciles de actualizar, haciendo posible establecer diferencias entre las distintas versiones que ha presentado el producto software (Boussaidi et al, 2012;Van Geet et al, 2010).…”

Section: Producción De Softwareunclassified

Caracterización de los Contextos de Uso de la Ingeniería Inversa

Ríos

Herrera

2017

Inf. tecnol.

View full text Add to dashboard Cite

El objetivo de este trabajo fue definir un modelo de caracterización de los contextos de uso de la ingeniería inversa. Para lograrlo se utilizó la técnica de coincidencia de patrones para hacer análisis sobre los resultados de la revisión de la literatura. El modelo definido se aplicó para caracterizar los contextos de producción de software, seguridad informática, computación forense y educación. La utilidad del modelo se evaluó en dos escenarios aplicando la técnica del juicio de expertos, estableciendo la disponibilidad de los artefactos software y el nivel de relevancia de las vistas arquitectónicas recuperadas. Se concluye que el modelo de caracterización definido sirve como referente para establecer estrategias de ingeniería inversa, orientadas a obtener resultados pertinentes al contexto en el cual se presenta la necesidad de realizar procesos de ingeniería inversa.

show abstract

Redocumentation of a legacy banking system

Cited by 11 publications

References 7 publications

Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

Applying AI in Practice: Key Challenges and Lessons Learned

Caracterización de los Contextos de Uso de la Ingeniería Inversa

Contact Info

Product

Resources

About