ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research

EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews

Metrics for agile requirements definition and management

“You Can't Manage What You Don't Measure” (Origin unknown) was the starting point for this research. The goal of this research was to define metrics to support and monitor the requirements defi-nition and management in the Alusta P2P Invoice automation and Procurement product de-velopment in the target organization. The research was conducted as a constructive research including document analysis, inter-views and facilitated workshop and it was done during June 2016-December 2016. Theory around agile software development, agile requirements definition and management and metrics was gathered to support the construct. First the research defined the requirements definition and management process for the target organization to mirror the metrics against it. The most important measure found during the research was whether the feature is validated with users and enhanced accordingly before implementation or not. With this metric or gate keeper it would be possible to use the customer acceptance as the key measure and in-crease the validated learning about customers as lean movement suggests. Minimum mar-ketable feature sets could be validated with users too. In the target organization it would be important to deploy the new design process well in use. To monitor the success, completing the steps on feature level could be measured to view the trend of improvement and it’s impact to the feature quality and efficiency. Enhancing the data analytics of the service production data would improve both the RDM process and the product quality and cost-efficiency. Through following the feature quality, it would be possible to explore and find the lean, waste-less way to do the discovery of the requirements via comparing the used RDM techniques and completed process phases to the quality of the outcome of the feature. Defining the RDM specific objectives against the current KPI’s could help to achieve better results with them. Evaluating business value and measuring organizational learning were left as areas of future research.“Et voi johtaa sitä mitä et mittaa” (Alkuperä tuntematon) oli tämän tutkimuksen lähtökohta. Tutkimuksen tavoite oli määritellä mittareita tukemaan ja tarkkailemaan vaatimusmäärittelyä ja vaatimusten hallingaa Alusta P2P Laskuautomaation ja Hankintojen hallinnan tuotekehityksessä kohdeorganisaatiossa. Tutkimus on konstruktiivinen ja se on toteutetttu dokumenttianalyysien, haastattelujen ja fasilitoidun työpajan avulla. Tutkimus on tehty Kesäkuun 2016 ja Joulukuun 2016 välillä. Teoriaa konstruktia tukemaan on kerätty ketterän sovelluskehityksen, ketterän vaatimusmäärittelyn ja vaatimusten hallinnan ja mittareiden alueelta. Aluksi tutkimus määritti vaatimusmäärittelyn ja -hallinnan prosessin kohdeyritykselle, jotta mittareita olisi helppo peilata sitä vasten. Tärkein tutkimuksen aikana löydetty mittari on onko toiminto vahvistettu käyttäjien kanssa ja tarvittavat korjaukset tehty ennen kehittämisen aloitusta. Tällä mittarilla tai portinvartijalla voidaan saavutaa asiakashyväksyntä tärkeimpänä mittarina ja lisätä leanin ehdottamaa vahvistettua asiakkaista oppimista. Pienimmät markkinotavat toimintokokonaisuudet (Minimum marketable feature) voitaisiin myös vahvistaa käyttäjien kanssa. Kohdeorganisaatiossa tärkeä kehityskohde olisi jalkauttaa uusi prosessi hyvin. Prosessin vaiheiden toteutumista voitaisiin seurata toimintokokonaisuuksien tasolla ja seurata kehitysvaiheiden suoritustason vaikutusta toimintojen laatuun ja tekemisen tehokkuuteen. Palvelun käyttödatan analysoinnin kehittäminen parantaisis vaatimuusmäärityksen ja vaatimsuten hallinnan prosessia ja tuotteen laatua ja tekemisen tehokkutta. Toimintokokoonaisuuksien laadun seuraamisen avulla olisi mahdollista etsiä ja löytää lean, hukaton tapa tehdä vaatimusten löytämisvaihetta vertaamalla käytettyjä vaatimusmäärittelyn ja -hallinnan tekniikoita saavutettuun tuloksen laatuun. Vaatimusmäärittelyn ja hallinnan päämäärät voitaisiin määrittää tarkemmin nykyisiä KPI:tä vastaan ja näin saavuttaa mittareiden parempi laatu. Liiketoiminnan tuottaman arvon määrittäminen ja organisatorisen oppimisen mittaaminen on jätetty jatkotutkimuksen aiheiksi