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.

This thesis aims to develop a knowledge structuring framework to organize knowledge according to means-ends relationships. Means-ends relationships are particularly relevant in technology and goal-oriented domains such as the geo-engineering domain, where technical problems are identified, and solutions proposed. The proposed goal oriented representation in this thesis does not replace current classification methodologies. In this project, a small corpus of research publications from a technology domain is used to help construct the framework. The main means-ends relationships from the articles are manually extracted and represented in a graphical model showing which problems are approached, by which solutions proposed, and in which publications. Proposed solutions can lead to new problems which are in turn addressed by solutions proposed in other publications. A metamodel is derived to capture the important concepts and relationships relevant for this purpose. The metamodel, and the framework have undergone several iterations before finalization.

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.

The management of technology portfolios is an important element of aerospace system design. New technologies are often applied to new product designs to ensure their competitiveness at the time they are introduced to market. The future performance of yet-to-be designed components is inherently uncertain, necessitating subject matter expert knowledge, statistical methods and financial forecasting. Estimates of the appropriate parameter settings often come from disciplinary experts, who may disagree with each other because of varying experience and background. Due to inherent uncertain nature of expert elicitation in technology valuation process, appropriate uncertainty quantification and propagation is very critical. The uncertainty in defining the impact of an input on performance parameters of a system, make it difficult to use traditional probability theory. Often the available information is not enough to assign the appropriate probability distributions to uncertain inputs. Another problem faced during technology elicitation pertains to technology interactions in a portfolio. When multiple technologies are applied simultaneously on a system, often their cumulative impact is non-linear. Current methods assume that technologies are either incompatible or linearly independent. It is observed that in case of lack of knowledge about the problem, epistemic uncertainty is most suitable representation of the process. It reduces the number of assumptions during the elicitation process, when experts are forced to assign probability distributions to their opinions without sufficient knowledge. Epistemic uncertainty can be quantified by many techniques. In present research it is proposed that interval analysis and Dempster-Shafer theory of evidence are better suited for quantification of epistemic uncertainty in technology valuation process. Proposed technique seeks to offset some of the problems faced by using deterministic or traditional probabilistic approaches for uncertainty propagation. Non-linear behavior in technology interactions is captured through expert elicitation based technology synergy matrices (TSM). Proposed TSMs increase the fidelity of current technology forecasting methods by including higher order technology interactions. A test case for quantification of epistemic uncertainty on a large scale problem of combined cycle power generation system was selected. A detailed multidisciplinary modeling and simulation environment was adopted for this problem. Results have shown that evidence theory based technique provides more insight on the uncertainties arising from incomplete information or lack of knowledge as compared to deterministic or probability theory methods. Margin analysis was also carried out for both the techniques. A detailed description of TSMs and their usage in conjunction with technology impact matrices and technology compatibility matrices is discussed. Various combination methods are also proposed for higher order interactions, which can be applied according to the expert opinion or historical data. The introduction of technology synergy matrix enabled capturing the higher order technology interactions, and improvement in predicted system performance.

A formal specification language used during the requirements phase can reduce errors and rework, but formal specifications are regarded as expensive to maintain, discouraging their adoption. This work presents a single-subject experiment that explores the costs of modifying specifications written in two different languages: a tabular notation, Software Cost Reduction (SCR), and a state-of-the-practice notation, Real-time Unified Modeling Language (UML). The study records the person-hours required to write each specification, the number of defects made during each specification effort, and the amount of time repairing these defects. Two different problems are specified&mdash;a Bidirectional Formatter (BDF), and a Bicycle Computer (BC)&mdash;to balance a learning effect from specifying the same problem twice with different specification languages. During the experiment, an updated feature for each problem is sent to the subject and each specification is modified to reflect the changes. <br /><br /> The results show that the cost to modify a specification are highly dependent on both the problem and the language used. There is no evidence that a tabular notation is easier to modify than a state-of-the-practice notation. <br /><br /> A side-effect of the experiment indicates there is a strong learning effect, independent of the language: in the BDF problem, the second time specifying the problem required more time, but resulted in a better-quality specification than the first time; in the BC problem, the second time specifying the problem required less time and resulted in the same quality specification as the first time. <br /><br /> This work demonstrates also that single-subject experiments can add important information to the growing body of empirical data about the use of formal requirements specifications in software development.

Open source projects do have requirements; they are, however, mostly informal, text descriptions found in requests, forums, and other correspondence. Understanding such requirements provides insight into the nature of open source projects. Unfortunately, manual analysis of natural language requirements is time-consuming, and for large projects, error-prone. Automated analysis of natural language requirements, even partial, will be of great benefit. Towards that end, I describe the design and validation of an automated natural language requirements classifier for open source software development projects. I compare two strategies for recognizing requirements in open forums of software features. The results suggest that classifying text at the forum post aggregation and sentence aggregation levels may be effective. Initial results suggest that it can reduce the effort required to analyze requirements of open source software development projects. Software development organizations and communities currently employ a large number of software development techniques and methodologies. This implied complexity is also enhanced by a wide range of software project types and development environments. The resulting lack of consistency in the software development domain leads to one important challenge that researchers encounter while exploring this area: specificity. This results in an increased difficulty of maintaining a consistent unit of measure or analysis approach while exploring a wide variety of software development projects and environments. The problem of specificity is more prominently exhibited in an area of software development characterized by a dynamic evolution, a unique development environment, and a relatively young history of research when compared to traditional software development: the open-source domain. While performing research on open source and the associated communities of developers, one can notice the same challenge of specificity being present in requirements engineering research as in the case of closed-source software development. Whether research is aimed at performing longitudinal or cross-sectional analyses, or attempts to link requirements to other aspects of software development projects and their management, specificity calls for a flexible analysis tool capable of adapting to the needs and specifics of the explored context. This dissertation covers the design, implementation, and evaluation of a model, a method, and a software tool comprising a flexible software development analysis framework. These design artifacts use a rule-based natural language processing approach and are built to meet the specifics of a requirements-based analysis of software development projects in the open-source domain. This research follows the principles of design science research as defined by Hevner et. al. and includes stages of problem awareness, suggestion, development, evaluation, and results and conclusion (Hevner et al. 2004; Vaishnavi and Kuechler 2007). The long-term goal of the research stream stemming from this dissertation is to propose a flexible, customizable, requirements-based natural language processing software analysis framework which can be adapted to meet the research needs of multiple different types of domains or different categories of analyses.

A formal specification language used during the requirements phase can reduce errors and rework, but formal specifications are regarded as expensive to maintain, discouraging their adoption. This work presents a single-subject experiment that explores the costs of modifying specifications written in two different languages: a tabular notation, Software Cost Reduction (SCR), and a state-of-the-practice notation, Real-time Unified Modeling Language (UML). The study records the person-hours required to write each specification, the number of defects made during each specification effort, and the amount of time repairing these defects. Two different problems are specified&mdash;a Bidirectional Formatter (BDF), and a Bicycle Computer (BC)&mdash;to balance a learning effect from specifying the same problem twice with different specification languages. During the experiment, an updated feature for each problem is sent to the subject and each specification is modified to reflect the changes. <br /><br /> The results show that the cost to modify a specification are highly dependent on both the problem and the language used. There is no evidence that a tabular notation is easier to modify than a state-of-the-practice notation. <br /><br /> A side-effect of the experiment indicates there is a strong learning effect, independent of the language: in the BDF problem, the second time specifying the problem required more time, but resulted in a better-quality specification than the first time; in the BC problem, the second time specifying the problem required less time and resulted in the same quality specification as the first time. <br /><br /> This work demonstrates also that single-subject experiments can add important information to the growing body of empirical data about the use of formal requirements specifications in software development.

<p>Detta examensarbete innehåller en introduktion till informationssystemutveckling och Requirements Engineering (RE). Vikten av att utföra RE på ett effektivt sätt är stor. Hela informationssystemet blir lidande om denna del inte utförs på ett tillfredställande sätt. För att utföra RE finns en rad tekniker och verktyg. Detta arbete behandlar en teknik i RE, nämligen Use Case-modellering. Arbetets syfte är att finna styrkor respektive svagheter med tekniken Use Case-modellering. Dessutom påvisar arbetet hur Use Case-modellering eventuellt kan förbättras.</p><p>Det som framkommer i rapporten visar hur litteraturen ser på Use Case-modellering samt hur personer som praktiskt har använt sig av tekniken ser på den. Resultatet belyser att litteraturen och praktiken har ungefär samma syn på Use Case-modellerings styrkor och svagheter och att tekniken bör förbättras på olika sätt. Arbetet avslutas med en diskussion kring genomförandet av arbetet samt en diskussion kring det uppnådda resultatet.</p>

<p>Use Case-tekniken används för kravutvinning under RE-processen. Under RE-processen utvinns och dokumenteras krav på det nya systemet. I detta arbete undersöks vilka oklarheter systemutvecklare som använder Use Case-tekniken upplever med beskrivningen av denna teknik.</p><p>Hur Use Case-tekniken utvecklats och vilken kritik som riktats mot tekniken beskrivs även detta i arbetet.</p><p>Undersökningen utgår ifrån hur de systemutvecklare som använder tekniken upplever att denna är beskriven och utfördes med enkäter och intervjuer.</p><p>Svaret på undersökningen är att de systemutvecklare som använder Use Case-tekniken upplever att denna som helhet är tydligt beskriven. På detaljnivå upplever dock de svarande att teknikens beskrivning:</p><p>* saknar stöd för på vilken nivå Use Cases skall utformas,</p><p>* saknar bra exempel,</p><p>* inte har tillräckligt fokus på syftet med Use Cases,</p><p>* saknar stöd för hur Use Cases skall utformas,</p><p>* inte beskriver skillnaden mellan generalisering och <<extend>></p><p>* inte beskriver skillnaden mellan <<include>> och <<extend>> tillräckligt väl.</p>

<p>Det problemområde som behandlas i arbetet är den del av systemutvecklarens arbete avsett att hantera förändringar i kravbilden under systemutvecklingens hela livscykel, kallat Requirements Management (RM). Arbetet fokuseras på detaljer kring spårbarhet inom förändringshantering.</p><p>För att lyckas i spårbarhetsarbetet gäller det för systemutvecklaren att se hur de olika krav som finns i kravbilden är relaterade till varandra och var kraven har sitt ursprung. Det skiljs inom spårbarhetsområdet mellan framåt- och bakåtriktad spårbarhet. Vardera spårbarhetsriktningen delas sedan in i två steg från kravursprung framåt via kravdokumentation till realisering då det gäller den framåtriktade och från realisering via kravdokumentation till kravursprung då det gäller den bakåtriktade.</p><p>En studie har genomförts där litteraturens syn har jämförts med ett antal respondenters då det gäller att dels kartlägga vilka typer av spårbarhet som behövs inom systemutvecklingsprojekt och dels vilket stöd som behövs för att etablera och underhålla denna spårbarhet.</p>