It is widely believed among researchers as well as practitioners that there is a link between new product newness, or innovativeness, and benefits to the firm developing and marketing a product; more innovative products are generally expected to create more profit and growth. However, research findings are conflicting—positive-, negative-, and no-relationship have been reported between product newness and benefits by different researchers. Moreover, most research has been confined to hardware products. Software is a different kind of product. It is marked by low industry entry barrier, low marginal cost of production, intense competition for quick market leadership, subject to increasing rate of return, et al. An ever larger part of investments in new products consist of computer software, software that is used in PCs, control industrial processes and give products like mobile phones, cameras and cars new features. To what extent newness gives benefits in software development is however still un-researched. Thus, the purpose this study was formulated as: To explore effect of newness of new software products on the benefits to the firms. To fulfill this research purpose, first we had to find out “What are the relevant elements of (i) newness, and (ii) benefits of new products” in the context of firms that develop and market computer software products? This part of the study is reported in Part I. In a second step the effect of product newness on benefits was investigated quantitatively. This part of the study is reported in Part II. Part I is based upon semi-structured in-depth interviews of managers responsible for seven new software products in firms from Finland, India, Sweden and the US. Supplementary secondary data were collected from archival sources to write case descriptions of each software product. Within- and cross-case inductive analysis of seven-case database led to identification of relevant elements of newness and benefits. As newness elements, distribution technology, and complementary technological-, and marketing-resources were found to be vital; as benefits element, non-monetary benefits of new products stood out. Part II reports a quantitative study involving 321 Swedish software firms. Data were collected through a Web-survey, using a questionnaire based on findings of Part I, and analyzed through Factor Analysis and Structural Equation Modeling. Findings indicate that marketing fit, and technological familiarity enhance product-level benefits, whereas technological fit, and familiarity enhance firm-level benefits. From the three environmental factors only aggressive marketing practices was found to be of significance. Neither switching costs nor computer mediated transactions was found to have any moderating role on product newness and new product benefits relationship. Overall, this study extends previous research in the area of product newness-new product benefits and fills the gap in the literature (i) by developing grounded measures for operationalizing new product newness and benefits concepts in the context of software product firms, and (ii) by identifying significant elements of new product newness that affect new product benefits. By limiting to a particular industry, this study provides useful findings—for both researches of new product development, and for managers in software firms—such as marketing fit, and technological familiarity enhance product-level benefits, whereas technological fit, and technological familiarity enhance firm-level benefits.

A software product line (SPL) succeeds because we exploit the similarities between a set of software-intensive systems, together with an understanding of their differences, to reduce development cost, maintenance cost, and user confusion. In SPL engineering, reuse is planned, enabled, and enforced. It is through the development of a set of core assets that reuse is systematically practiced. Requirements assets enhance the effectiveness of reuse since engineers can work on the abstractions closer to the systems' initial concepts. Contemporary SPL requirements engineerin (RE) approaches often adopt the proactive model to build a relatively complete and stable asset base. In practice, the substantial up-front effort and the abrupt transition from existing practices associated with the proactive model present a prohibitive SPL adoption barrier for many organizations that could otherwise benefit. The extractive model overcomes these shortcomings by reusing existing products for the SPL's initial baseline. In this thesis, we present a framework for applying lightweight techniques to extract, model, and analyze a SPL's requirements assets. We define the notion of functional requirements profiles (FRPs) according to the linguistic characterization of a domain's action-oriented concerns, and show that the FRPs can be extracted from a natural language document on the basis of domain-aware lexical affinities that bear a 'verb - direct object' relation. We model the extracted FRPs by analyzing their semantic cases and by extending the orthogonal variability model (OVM). We contribute a set of heuristic rules for uncovering the variation dimensions and dependencies, and discuss merging the OVMs extracted from multiple sources. We relate functional profiles to quality requirements via scenarios, and manage requirements interactions via concept analysis. We present two applications of FRPs to support some other activities in SPL engineering. We conduct several empirical studies to evaluate our framework. The results show that our approach allows the engineers to identify the domain elements more easily and develop the domain models more systematically. Our work fills the void with respect to extracting a SPL's requirements assets, and the main thrust of our work is to promote a set of lightweight, low adoption threshold techniques as a critical enabler for practitioners to capitalize on the order-of-magnitude improvements offered by SPL engineering.

A software product line (SPL) consists of a set of software-intensive systems sharing a common, managed set of features that satisfy the specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way. Together with testing of final deliverable products developed within the SPL, called Integration Testing, particularly important in this context is the way individual hardware as well as software components in an SPL are tested and certified for usage within the SPL. This study investigates specific approaches and techniques proposed in the literature for unit testing in the SPL context. Problems inherent to this issue were studied and possible solutions aiming towards systematic and effective testing of hardware as well as software units in SPLs have been proposed. The specific problems of SPL testing in ASELSAN were investigated in the light of these possible solutions and their applicability as well as their benefits were quantitatively assessed.

A software product line (SPL) succeeds because we exploit the similarities between a set of software-intensive systems, together with an understanding of their differences, to reduce development cost, maintenance cost, and user confusion. In SPL engineering, reuse is planned, enabled, and enforced. It is through the development of a set of core assets that reuse is systematically practiced. Requirements assets enhance the effectiveness of reuse since engineers can work on the abstractions closer to the systems' initial concepts. Contemporary SPL requirements engineerin (RE) approaches often adopt the proactive model to build a relatively complete and stable asset base. In practice, the substantial up-front effort and the abrupt transition from existing practices associated with the proactive model present a prohibitive SPL adoption barrier for many organizations that could otherwise benefit. The extractive model overcomes these shortcomings by reusing existing products for the SPL's initial baseline. In this thesis, we present a framework for applying lightweight techniques to extract, model, and analyze a SPL's requirements assets. We define the notion of functional requirements profiles (FRPs) according to the linguistic characterization of a domain's action-oriented concerns, and show that the FRPs can be extracted from a natural language document on the basis of domain-aware lexical affinities that bear a 'verb - direct object' relation. We model the extracted FRPs by analyzing their semantic cases and by extending the orthogonal variability model (OVM). We contribute a set of heuristic rules for uncovering the variation dimensions and dependencies, and discuss merging the OVMs extracted from multiple sources. We relate functional profiles to quality requirements via scenarios, and manage requirements interactions via concept analysis. We present two applications of FRPs to support some other activities in SPL engineering. We conduct several empirical studies to evaluate our framework. The results show that our approach allows the engineers to identify the domain elements more easily and develop the domain models more systematically. Our work fills the void with respect to extracting a SPL's requirements assets, and the main thrust of our work is to promote a set of lightweight, low adoption threshold techniques as a critical enabler for practitioners to capitalize on the order-of-magnitude improvements offered by SPL engineering.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.

<p>This report presents the results of a thesis project that surveys and designs about the issue “Feature Model for Adaptive Computing”. In this project, there are two main issues, first one is about the Feature Modeling, and the second is how to use this Feature Modeling for adaptive computing.</p><p>In this thesis report, at the beginning, we present the problem we expected to solve and introduce some background information, including the knowledge of feature model and adaptive computing. Then we explain our solution and evaluate this solution. At the end of this report, we give a short conclusion about our thesis project and feature work.</p>

A Software Product Line (SPL) is a family of related programs, which of each is defined by a combination of features. By developing related programs together, an SPL simultaneously reduces programming effort and satisfies multiple sets of requirements. Testing an SPL efficiently is challenging because a property must be checked for all the programs in the SPL, the number of which can be exponential in the number of features. In this dissertation, we present a suite of complementary static and dynamic techniques for efficient testing and runtime monitoring of SPLs, which can be divided into two categories. The first prunes programs, termed configurations, that are irrelevant to the property being tested. More specifically, for a given test, a static analysis identifies features that can influence the test outcome, so that the test needs to be run only on programs that include these features. A dynamic analysis counterpart also eliminates configurations that do not have to be tested, but does so by checking a simpler property and can be faster and more scalable. In addition, for runtime monitoring, a static analysis identifies configurations that can violate a safety property and only these configurations need to be monitored. When no configurations can be pruned, either by design of the test or due to ineffectiveness of program analyses, runtime similarity between configurations, arising due to design similarity between configurations of a product line, is exploited. In particular, shared execution runs all the configurations together, executing bytecode instructions common to the configurations just once. Deferred execution improves on shared execution by allowing multiple memory locations to be treated as a single memory location, which can increase the amount of sharing for object-oriented programs and for programs using arrays. The techniques have been evaluated and the results demonstrate that the techniques can be effective and can advance the idea that despite the feature combinatorics of an SPL, its structure can be exploited by automated analyses to make testing more efficient. / text

Research in Software Product Line Engineering (SPLE) traditionally focuses on product derivation. Prior work has explored the automated derivation of products by module composition. However, it has so far neglected propagating changes (edits) in a product back to the product line definition. A domain-specific product should be possible to update its features locally, and later these changes should be propagated back to the product line definition automatically. Otherwise, the entire product line has to be revised manually in order to make the changes permanent. Although this is the current state, it is a very error-prone process. To address these issues, we present a tool called Paan to create product lines of MS Word documents with back-propagation support. It is a diff-based tool that ignores unchanged fragments and reveals fragments that are changed, added or deleted. Paan takes a document with variation points (VPs) as input, and shreds it into building blocks called tiles. Only those tiles that are new or have changed must be updated in the tile repository. In this way, changes in composed documents can be back-propagated to their original feature module definitions. A document is synthesized by retrieving the appropriate tiles and composing them. / text

Consumer preference measurement is a quantitative field of study for modeling, collecting and analyzing product decisions by consumers. Discovering how consumers choose products is an important area of marketing research and recognized as a successful partnership between academic theory and practice over the past forty years. Despite preference measurement’s success in consumer products, little guidance is available for its application to software product management. This paper assesses the feasibility of applying advanced preference measurement techniques to software products and suggests a framework for conducting such studies. A summary of the methods is provided to give guidance to software product managers seeking to apply preference measurement to common product decisions. The paper concludes by recommending a technique called ‘maximum difference scaling’ to elicit customer feedback to help measure the importance of new features for software product improvement. / text