Abstract Software process improvement requires significant effort related not only to the identification of relevant issues and providing an adequate response to them, but also to the implementation and adoption of the changes. Best practices provide recommendations to software teams on how to address identified objectives in practice, based on aggregated experience and knowledge. In the paper, we present the GÉANT SPI framework based on best practices, together with the collected experience from the process of adopting the best practices.

Pattern structures were introduced by Ganter and Kuznetsov in the framework of formal concept analysis (FCA) as a mean to direct analysis of objects having complex descriptions, e.g., descriptions presented in the form of graphs instead of a set of properties. Pattern structures actually generalise /replace the original FCA representation of the initial information about objects, that is, formal contexts (which form a special type of data tables); as a consequence, pattern structures are regarded in FCA as given (in some sense a priori to the analysis) rather than built (a posteriori) from data. The main goal of this paper is twofold: firstly, we would like to export the idea of pattern structures to and consistently with the framework/methodology of rough set theory (RST); secondly, we want to derive pattern structures from simple data tables rather than to regard them as the initial information about objects. To this end we present and discuss two methods of generating non-trivial pattern structures from simple information systems/tables. Both methods are inspired by near set theory, which is a methodology theoretically close to rough set theory, but developed in the topological settings of (descriptive) nearness of sets. Interestingly, these methods bear formal connections to other ideas from RST such as generalised decisions or symbolic value grouping.

Abstract In the paper, we present a measurement framework for evaluating quality in software products developed within the research and innovation framework project GÉANT. The proposed framework is based on the quality models by Boehm and McCall, but also addresses the presence and point of view of a third stakeholder: an external funding agency (EU), which has started and is temporally financing the project, but aims at making it self‐financing in the future. We also provide results of evaluation of 2 projects from the GÉANT ecosystem and one open‐source system with this framework.

Hierarchical software quality models usually rely on a number of metrics, which, once aggregated, provide an overview of selected perspectives of a system's quality. Missing values of some metrics, that usually result from data unavailability, can seriously affect the final score. In the paper we empirically validate a few imputation methods in context of a custom Géant-QM framework, used for evaluation of several open source systems. Early results indicate imputing a missing value based on its close neighbors as data donors introduces less noise that using a wider set of donors.

Hierarchical software quality models define different levels, at which various criteria or characteristics are evaluated. In order to combine the data from lower levels, we need an effective method of aggregation. In this paper we report observations and conclusions from applying several approaches to metric aggregation, based on data acquired from two software systems.

This volume comprises papers presented at the Sixth Rough Set Theory Workshop (RST'2015), which was held on 29 June 2015 at the University of Warsaw. In the last few decades we have been witnessing a rapid development of rough set theory, and considerable evolution of views on its foundations. The RST meetings were initiated in Milan (

The paper studies the rough granular computing paradigm within the conceptual settings of multi-modal logic. The main idea is to express a generalised approximation space (U; I; κ), where U is the universe of objects, I is an uncertainty function, and κ is a rough inclusion function, in terms of bi nary relations, and then to consider the corresponding modal operators. The new modal structure obtained in this way is rich enough to define closure and interior operators corresponding to the classical rough approximation operators and their well-known uni-modal generalisations. In contrast to the standard modal interpretation of rough set approximations, in the new settings one can directly deal with information granules and their properties, which is crucial for granular computing paradigm. More precisely, we are provided with means of describing features of objects and information granules, as well as inclusion degrees between granules.

Rough inclusion functions are mappings considered in rough set theory with which one can measure the degree of inclusion of a set (information granule) in a set (information granule) in line with rough mereology. On the other hand, similarity indices

The paper addresses the problem of concept formation (knowledge granulation) in the settings of rough set theory. The original version of rough set theory implicitly accommodates a lot of well-established philosophical assumptions about concept formation as presented by A. Rand. However, as suggested by S. Hawking and L. Mlodinow, one has also to consider the dynamics of the universe of objects and different scales at which concepts may be formed. These both aspects have already been discussed separately in rough set theory. Different forms of dynamics have been addressed explicitly – especially the case of extending the universe by new objects; in contrast, different scales of description have been addressed implicitly, mainly within the Granular Computing (GrC) paradigm. Following the example of Life, the famous game invented by J. Conway, we describe the corresponding dynamics in Pawlak information systems using a GrC driven methodology. Having dynamics discussed, we address the problem of concept formation at zoom-out scales of description. To this end, we build Scott systems as information systems describing the universe at a coarser scale than the original scale of Pawlak systems. We regard these systems as a special type of classifications, which have already been studied in the context of rough sets by A. Skowron et al.

Representation theory is a branch of mathematics whose original purpose was to represent information about abstract algebraic structures by means of methods of linear algebra (usually, by linear transformations and matrices). G.-C. Rota in his famous

In this article we present three inclusion functions which characterise the nearness relation between finite sets of objects defined in line with J. F. Peters, A. Skowron, and J. Stepaniuk [26]. By means of these functions we extend the notion of nea

The paper studies complete and incomplete information systems, i.e. basic structures of rough set theory (RST), from the standpoint of monadic Boolean algebras (MBAs). In the first part we recall a simple characterisation of RST as a theory of MBAs.

In the paper we discuss logical approaches to incomplete and/or nondeterministic data. As is well-known, complete and deterministic information systems induce indiscernibility relations and the lower and upper approximations regarded as operators obe

The paper aims to establish topological links between perception of objects (as it is defined in the framework of near sets) and classification of these objects (as it is defined in the framework of rough sets). In the near set approach, the discovery of near sets (i.e. sets containing objects with similar descriptions) starts with the selection of probe functions which provide a basis for describing and discerning objects. On the other hand, in the rough set approach, the classification of objects is based on object attributes which are collected into information systems (or data tables). As is well-known, an information system can be represented as a topological space (U, τ E ). If we pass froman approximation space (U,E) to the quotient space U/E, where points represent indiscernible objects of U, then U/E will be endowed with the discrete topology induced (via the canonical projection) by τ E . The main objective of this paper is to show how probe functions can provide new topologies on the quotient set U/E and, in consequence, new (perceptual) topologies on U.