Dr.-Ing.

Jedlitschka, Andreas

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  • Publication
    Continuously Assessing and Improving Software Quality With Software Analytics Tools: A Case Study
    ( 2019)
    Martínez-Fernández, Silverio
    ;
    Vollmer, Anna Maria
    ;
    Jedlitschka, Andreas
    ;
    Franch, Xavier
    ;
    López, Lidia
    ;
    Ram, Prabhat
    ;
    Rodríguez, Pilar
    ;
    Aaramaa, Sanja
    ;
    Bagnato, Alessandra
    ;
    Choras, Michal
    ;
    Partanen, Jari
    In the last decade, modern data analytics technologies have enabled the creation of software analytics tools offering real-time visualization of various aspects related to software development and usage. These tools seem to be particularly attractive for companies doing agile software development. However, the information provided by the available tools is neither aggregated nor connected to higher quality goals. At the same time, assessing and improving the software quality has also been the key targets for the software engineering community, yielding several proposals for standards and software quality models. Integrating such quality models into software analytics tools could close the gap by providing the connection to higher quality goals. This paper aims at understanding whether the integration of quality models into software analytics tools provides understandable, reliable, useful, and relevant information at the right level of detail about the quality of a process or product and whether practitioners intend to use it. Over the course of more than a year, four companies involved in this case study deployed such a tool to assess and improve software quality in several projects. We used standardized measurement instruments to elicit the perception of 22 practitioners regarding their use of the tool. We complemented the findings with debriefing sessions held at the companies. In addition, we discussed challenges and lessons learned with four practitioners leading the use of the tool. The quantitative and qualitative analyses provided positive results, i.e., the practitioners' perception with regard to the tool's understandability, reliability, usefulness, and relevance was positive. Individual statements support the statistical findings, and constructive feedback can be used for future improvements. We conclude that the potential for future adoption of quality models within software analytics tools definitely exists and encourage other practitioners to use the presented seven challenges and seven lessons learned and adopt them in their companies.
  • Publication
    Quality-aware architectural model transformations in adaptive mashups user interfaces
    ( 2018)
    Criado, Javiera
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    Martínez-Fernández, Silverio
    ;
    Ameller, David
    ;
    Iribarne, Luis
    ;
    Padilla, Nicolás
    ;
    Jedlitschka, Andreas
    Mashup user interfaces provides their functionality through the combination of different services. The integration of such services can be solved by using reusable and third-party components. Furthermore, these interfaces must be adapted to user preferences, context changes, user interactions and component availability. Model transformation is a useful mechanism to address this adaptation but normally these operations only focus on the functional requirements. In this sense, quality attributes should be included in the adaptation process to obtain the best adapted mashup user interface. This paper proposes a generic quality-aware transformation process to support the adaptation of software architectures. The transformation process has been applied in ENIA, a geographic information system, by constructing a specific quality model for the adaptation of mashup user interfaces. This model is taken into account for evaluating the different transformation alternatives and choosing the one that maximizes the quality assessments. The approach has been validated by a set of adaptation scenarios that are intended to maximize different quality factors and therefore apply distinct combinations of metrics.