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Ο όρος Πληροφοριακό Σύστημα (Information System), αναφέρεται σε ένα σύστημα που περικλείει όλες τις λειτουργίες ενός οργανισμού. Το βασικό συστατικό του είναι το λογισμικό που χρησιμοποιείται σε έναν οργανισμό για την αποθήκευση και επεξεργασία δεδομένων και την αυτοματοποίηση πληροφοριακών διαδικασιών. Η πολυπλοκότητα της δομής του λογισμικού, δημιουργούν ένα εξαιρετικά σύνθετο πρόβλημα που απαιτεί προηγμένες μεθόδους ανάλυσης και έρευνας.Η περιοχή στην οποία εστιάζεται κυρίως η παρούσα διδακτορική διατριβή είναι ο καθορισμός προτεραιοτήτων στις διάφορες απαιτήσεις του λογισμικού (requirements prioritization). Ο μεγάλος αριθμός των απαιτήσεων και οι διαφορετικές απόψεις των εργαζομένων που συμμετέχουν στην έρευνα και ορίζουν ποιες είναι οι πιο σημαντικές και επομένως αξίζουν υψηλότερη προτεραιότητα κάνουν το πρόβλημα της λήψης αποφάσεων ιδιαίτερα δύσκολο και πολύπλοκο. Τα δεδομένα που συλλέγονται από ειδικά ερωτηματολόγια (Cumulative Voting) που συμπληρώνουν οι συμμετέχοντες σε μια τέτοια έρευνα περιέχουν τις απόψεις τους κωδικοποιημένες σε αριθμητικές βαθμολογίες για την απόδοση προτεραιοτήτων στις διάφορες απαιτήσεις. Παρόλο που η απλή άθροιση των βαθμολογιών από όλους τους συμμετέχοντες δείχνει ποιες απαιτήσεις είναι οι πιο σημαντικές, η συνολική πληροφορία που υπάρχει στα δεδομένα κρύβει μεγάλη μεταβλητότητα και συσχετίσεις και δεν έχει μελετηθεί συστηματικά με στατιστικές μεθόδους. Οι μεταβλητές του συνόλου των δεδομένων, από τα συγκεκριμένα σχήματα ψηφοφορίας, είναι φραγμένες και μεταξύ τους συσχετισμένες. Στη γενική τους κανονικοποιημένη μορφή τα δεδομένα αυτά αποτελούνται από διανύσματα ποσοστών (στο διάστημα [0,1]) που αθροίζονται στη μονάδα. Επομένως απαιτούνται ειδικές μέθοδοι πολυμεταβλητής ανάλυσης για τέτοιου είδους δεδομένα, αφού οι συνήθεις υποθέσεις κανονικότητας και ανεξαρτησίας δεν ισχύουν εδώ. Οι μέθοδοι αυτές θα πρέπει να απαντήσουν σε διάφορα ερωτήματα που σχετίζονται με το πόσο συμφωνούν μεταξύ τους οι συμμετέχοντες, με την αντικρουόμενη φύση των απαιτήσεων, με την ομαδοποίηση των απόψεων και με τη σύγκριση των απόψεων ανάμεσα σε προκαθορισμένες ομάδες των ενδιαφερομένων. Στη διαχείριση έργων λογισμικού η κατάλληλη κατανομή των πόρων στις διάφορες φάσεις ανάπτυξης του έργου σε σχέση με τη διερεύνηση της υψηλής πολυπλοκότητας της κατανομής της προσπάθειας (effort) αποτελούν ενδιαφέροντα προβλήματα που δεν είχαν μελετηθεί με πολυμεταβλητές μεθόδους. Στην διδακτορική αυτή διατριβή εφαρμόστηκαν πολυμεταβλητές στατιστικές μέθοδοι ειδικές για δεδομένα ανάθεσης προτεραιοτήτων που δεν έχουν χρησιμοποιηθεί ποτέ μέχρι τώρα στη διεθνή βιβλιογραφία. Ενδεικτικά αναφέρεται η χρήση του πλαισίου στατιστικών αναλύσεων Ανάλυση Δεδομένων Σύστασης (Compositional Data Analysis, CoDA) και χρησιμοποιείται για δεδομένα ποσοστών παρόμοια με αυτά που λαμβάνονται από τις διαδικασίες ανάθεσης προτεραιοτήτων. Η ερευνητική προσπάθεια μέσα από αυτή τη διατριβή και από την στατιστική ανάλυση προσπάθησε να δώσει έμφαση στον ανθρώπινο παράγοντα και να διερευνήσει τις διαφορές και τις ομοιότητες στις απόψεις που τελικά καθορίζουν την ποιότητα ενός προϊόντος.Η έρευνα που διεξήχθη στα πλαίσια της παρούσας διδακτορικής διατριβής είχε ως στόχο τη χρήση προηγμένων στατιστικών μεθόδων και μοντέλων σε προβλήματα ποιότητας πληροφοριακών συστημάτων.

Context. Requirements engineering remains a discipline that is faced with a large number of challenges, including the implementation of a requirements elicitation process in industry. Although several proposals have been suggested by researchers and academics, little is known of the practices that are actually followed in industry. Objective. We investigate the SoTA with respect to requirements elicitation, examining practitioners' practices. We focus on the techniques, the roles involved, and the challenges associated to the process. Method. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. Results. We found that group interaction techniques, including meetings and workshops, are the most popular type of elicitation techniques that are employed by the practitioners, except in the case of small projects. We noted that customers are frequently involved in the elicitation process, except in the case of market-driven organizations. Technical staff (for example, developers and architects) are more frequently involved in the elicitation process compared to the involvement of business- or strategic staff. Finally, we identified a number of challenges with respect to stakeholders. These challenges include difficulties in understanding and prioritizing their needs. Further, it was noted that requirements instability (i.e., caused by changing needs or priorities) was a predominant challenge. These observations need to be interpreted in the context of the study. Conclusion. The relevant observations regarding the survey participants' experiences should be of interest to the industry; experiences that should be analyzed in the practitioners' context. Researchers may find evidence for the use of academic results in practice, thereby inspiring future theoretical work, as well as further empirical studies in the same area., 32 pages, Accepted for publication in the Requirements Engineering Journal, 2021. Cite as Palomares, C., Franch, X., Quer, C. et al. The state-of-practice in requirements elicitation: an extended interview study at 12 companies. Requirements Eng (2021)

Component-based software engineering is a common approach in the development and evolution of contemporary software systems. Different component sourcing options are available, such as: (1) Software developed internally (in-house), (2) Software developed outsourced, (3) Commercial off-the-shelf software, and (4) Open-Source Software. However, there is little available research on what attributes of a component are the most important ones when selecting new components. The objective of this study is to investigate what matters the most to industry practitioners when they decide to select a component. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using compositional data analysis. The results of this exploratory analysis showed that cost was clearly considered to be the most important attribute for component selection. Other important attributes for the practitioners were: support of the component, longevity prediction, and level of off-the-shelf fit to product. Moreover, several practitioners still consider in-house software development to be the sole option when adding or replacing a component. On the other hand, there is a trend to complement it with other component sourcing options and, apart from cost, different attributes factor into their decision. Furthermore, in our analysis, nonparametric tests and biplots were used to further investigate the practitioners' inherent characteristics. It seems that smaller and larger organizations have different views on what attributes are the most important, and the most surprising finding is their contrasting views on the cost attribute: larger organizations with mature products are considerably more cost aware. open access

With the increasing popularity of agile development and team-oriented practices, bottom-up coordination structures have found their ways into software companies, first changing the small companies and now revolutionizing large-scale development projects and programs. One of the ways to enable bottom-up coordination is cultivation of communities of practice. Existing research has demonstrated that successful implementation of communities of practice depends on organizational support, mutual engagement and regular interaction. Engagement is said to increase, when a community creates value for the organization and individual community members, while increased engagement is further associated with the ability to create more value. However, little is known about how to ensure member engagement in large-scale environments covering many sites and thousands of developers. In this article, we report our findings from studying member engagement in large-scale distributed communities of practice at Spotify called guilds. We report the perceived value guilds provide on individual and organizational level, and discuss what hinders and what stimulates mutual engagement and value creation across time and space. Open access

Background: Requirements engineering is of a principal importance when starting a new project. However, the number of the requirements involved in a single project can reach up to thousands. Controlling and assuring the quality of natural language requirements (NLRs), in these quantities, is challenging. Aims: In a field study, we investigated with the Swedish Transportation Agency (STA) to what extent the characteristics of requirements had an influence on change requests and budget changes in the project. Method: We choose the following models to characterize system requirements formulated in natural language: Concern-based Model of Requirements (CMR), Requirements Abstractions Model (RAM) and Software-Hardware model (SHM). The classification of the NLRs was conducted by the three authors. The robust statistical measure Fleiss’ Kappa was used to verify the reliability of the results. We used descriptive statistics, contingency tables, results from the Chi-Square test of association along with post hoc tests. Finally, a multivariate statistical technique, Correspondence analysis was used in order to provide a means of displaying a set of requirements in two-dimensional graphical form. Results: The results showed that software requirements are associated with less budget cost than hardware requirements. Moreover, software requirements tend to stay open for a longer period indicating that they are ”harder” to handle. Finally, the more discussion or interaction on a change request can lower the actual estimated change request cost. Conclusions: The results lead us to a need to further investigate the reasons why the software requirements are treated differently from the hardware requirements, interview the project managers, understand better the way those requirements are formulated and propose effective ways of Software management.

Context: Component-based software engineering (CBSE) is a common approach to develop and evolve contemporary software systems. When evolving a system based on components, make-or-buy decisions are frequent, i.e., whether to develop components internally or to acquire them from external sources. In CBSE, several different sourcing options are available: (1) developing software in-house, (2) outsourcing development, (3) buying commercial-off-the-shelf software, and (4) integrating open source software components. Objective: Unfortunately, there is little available research on how organizations select component sourcing options (CSO) in industry practice. In this work, we seek to contribute empirical evidence to CSO selection. Method: We conduct a cross-domain survey on CSO selection in industry, implemented as an online questionnaire. Results: Based on 188 responses, we find that most organizations consider multiple CSOs during software evolution, and that the CSO decisions in industry are dominated by expert judgment. When choosing between candidate components, functional suitability acts as an initial filter, then reliability is the most important quality. Conclusion: We stress that future solution-oriented work on decision support has to account for the dominance of expert judgment in industry. Moreover, we identify considerable variation in CSO decision processes in industry. Finally, we encourage software development organizations to reflect on their decision processes when choosing whether to make or buy components, and we recommend using our survey for a first benchmarking. © 2019

Simulation is a powerful instrument in the education process that can help students experience a reality context and understand complex concepts required to accomplish practitioners’ tasks. The present study aims to investigate the software engineering students’ perception about the usefulness of the Planning Poker technique in relation to their understanding of the relative estimation concept. We conducted a simulation exercise where students first estimated tasks applying the concepts of relative estimation based on the concepts explained in the lecture, and then to estimate tasks applying the Agile Planning Poker technique. To investigate the students’ perception, we used a survey at the end of each exercise. The preliminary results did not show statistical significance on the students’ confidence to estimate relatively the user stories. However, from the students’ comments and feedback, there are indications that students are more confident in using Agile Planning Poker when they are asked to estimate user stories. The study will be replicated in the near future to a different group of students with a different background, to have a better understanding and also identify possible flaws of the exercise.

Component-based software engineering is a common approach to develop and evolve contemporary software systems where different component sourcing options are available: 1)Software developed internally (in-house), 2)Software developed outsourced, 3)Commercial of the shelf software, and 4) Open Source Software. However, there is little available research on what attributes of a component are the most important ones when selecting new components. The object of the present study is to investigate what matters the most to industry practitioners during component selection. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using Compositional Data Analysis. The descriptive results showed that Cost was clearly considered the most important attribute during the component selection. Other important attributes for the practitioners were: Support of the component, Longevity prediction, and Level of off-the-shelf fit to product. Next, an exploratory analysis was conducted based on the practitioners' inherent characteristics. Nonparametric tests and biplots were used. It seems that smaller organizations and more immature products focus on different attributes than bigger organizations and mature products which focus more on Cost.

Context: Software start-ups are young companies aiming to build and market software-intensive products fast with little resources. Aiming to accelerate time-to-market, start-ups often opt for ad-hoc engineering practices, make shortcuts in product engineering, and accumulate technical debt. Objective: In this paper we explore to what extent precedents, dimensions and outcomes associated with technical debt are prevalent in start-ups. Method: We apply a case survey method to identify aspects of technical debt and contextual information characterizing the engineering context in start-ups. Results: By analyzing responses from 86 start-up cases we found that start-ups accumulate most technical debt in the testing dimension, despite attempts to automate testing. Furthermore, we found that start-up team size and experience is a leading precedent for accumulating technical debt: larger teams face more challenges in keeping the debt under control. Conclusions: This study highlights the necessity to monitor levels of technical debt and to preemptively introduce practices to keep the debt under control. Adding more people to an already difficult to maintain product could amplify other precedents, such as resource shortages, communication issues and negatively affect decisions pertaining to the use of good engineering practices.

Software Cost Estimation (SCE) is a critical phase in software development projects. However, due to the growing complexity of the software itself, a common problem in building software cost models is that the available datasets contain lots of missing categorical data. The purpose of this chapter is to show how a framework of statistical, computational, and visualization techniques can be used to evaluate and compare the effect of missing data techniques on the accuracy of cost estimation models. Hence, the authors use five missing data techniques: Multinomial Logistic Regression, Listwise Deletion, Mean Imputation, Expectation Maximization, and Regression Imputation. The evaluation and the comparisons are conducted using Regression Error Characteristic curves, which provide visual comparison of different prediction models, and Regression Error Operating Curves, which examine predictive power of models with respect to under- or over-estimation.

One of the most significant problems faced by project managers is to effectively distribute the project resources and effort among the various project activities. Most importantly, project success depends on how well, or how balanced, the work effort is distributed among the project phases. This paper aims to obtain useful information regarding the correlation of the composition of effort attributed in phases for around 1,500 software projects of the ISBSG R11 database based on a promising statistical method called Compositional Data Analysis (CoDA). The motivation for applying this analysis is the observation that certain types of project data (effort distributions and attributes) do not relate in a direct way but present a spurious correlation. Effort distribution is compared to the project life-cycle activities, organization type, language type, function points and other prime project attributes. The findings are beneficial for building a basis for software cost estimation and improving future empirical software studies. 2012 IEEE.

The development of a software product in an organization involves various groups of stakeholders who may prioritize the qualities of the product differently. This paper presents an empirical study of 65 individuals in different roles and in different locations, including on shoring, outsourcing and off shoring, prioritizing 24 software quality aspects. Hierarchical cluster analysis is applied to the prioritization data, separately for the situation today and the ideal situation, and the composition of the clusters, regarding the distribution of the inherent groupings within each of them, is analyzed. The analysis results in observing that the roles are not that important in the clustering. However, compositions of clusters regarding the onshore-offshore relationships are significantly different, showing that the offshore participants have stronger tendency to cluster together. In conclusion, stakeholders seem to form clusters of aligned understanding of priorities according to personal and cultural views rather than their roles in software development.

Background: Software quality issues are commonly reported when off shoring software development. Value-based software engineering addresses this by ensuring key stakeholders have a common understanding of quality. Aim: This work seeks to understand the levels of alignment between key stakeholders on aspects of software quality for two products developed as part of an offshore in sourcing arrangement. The study further aims to explain the levels of alignment identified. Method: Representatives of key stakeholder groups for both products ranked aspects of software quality. The results were discussed with the groups to gain a deeper understanding. Results: Low levels of alignment were found between the groups studied. This is associated with insufficiently defined quality requirements, a culture that does not question management and conflicting temporal reflections on the product's quality. Conclusion: The work emphasizes the need for greater support to align success-critical stakeholder groups in their understanding of quality when off shoring software development.

Software Cost Estimation is a critical phase in the development of a software project, and over the years has become an emerging research area. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. The purpose of this chapter is to show how a combination of modern statistical and computational techniques can be used to compare the effect of missing data techniques on the accuracy of cost estimation. Specifically, a recently proposed missing data technique, the multinomial logistic regression, is evaluated and compared with four older methods: listwise deletion, mean imputation, expectation maximization and regression imputation with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error and the comparisons are conducted using statistical tests, resampling techniques and a visualization tool, the regression error characteristic curves.