Jens Heidrich

Jens Heidrich, Andreas Jedlitschka, Adam Trendowicz et al.

In the future, most companies will be confronted with the topic of Artificial Intelligence (AI) and will have to decide on their strategy in this regards. Currently, a lot of companies are thinking about whether and how AI and the usage of data will impact their business model and what potential use cases could look like. One of the biggest challenges lies in coming up with innovative solution ideas with a clear business value. This requires business competencies on the one hand and technical competencies in AI and data analytics on the other hand. In this article, we present the concept of AI innovation labs and demonstrate a comprehensive framework, from coming up with the right ideas to incrementally implementing and evaluating them regarding their business value and their feasibility based on a company's capabilities. The concept is the result of nine years of working on data-driven innovations with companies from various domains. Furthermore, we share some lessons learned from its practical applications. Even though a lot of technical publications can be found in the literature regarding the development of AI models and many consultancy companies provide corresponding services for building AI innovations, we found very few publications sharing details about what an end-to-end framework could look like.

Jens Heidrich, Michael Kläs, Andreas Morgenstern et al.

In recent years, the role and the importance of software in the automotive domain have changed dramatically. Being able to systematically evaluate and manage software quality is becoming even more crucial. In practice, however, we still find a largely static approach for measuring software quality based on a predefined list of complexity metrics with static thresholds to fulfill. We propose using a more flexible framework instead, which systematically derives measures and evaluation rules based on the goals and context of a development project.

Julien Siebert, Lisa Joeckel, Jens Heidrich et al.

Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the system and its components (such as ISO/IEC 25010). Due to the different nature of ML, we have to adjust quality aspects or add additional ones (such as trustworthiness) and be very precise about which aspect is really relevant for which object of interest (such as completeness of training data), and how to objectively assess adherence to quality requirements. In this article, we present the construction of a quality model (i.e., evaluation objects, quality aspects, and metrics) for an ML system based on an industrial use case. This quality model enables practitioners to specify and assess quality requirements for such kinds of ML systems objectively. In the future, we want to learn how the term quality differs between different types of ML systems and come up with general guidelines for specifying and assessing qualities of ML systems.

Michael Kläs, Jens Heidrich, Jürgen Münch et al.

Managing quality (such as service availability or process adherence) during the development, operation, and maintenance of software(-intensive) systems and services is a challenging task. Although many organizations need to define, control, measure, and improve various quality aspects of their devel- opment artifacts and processes, nearly no guidance is available on how to select, adapt, define, combine, use, and evolve quality models. Catalogs of quality models as well as selection and tailoring processes are widely missing. One essential reason for this tremendous lack of support is that software development is a highly context-dependent process. Therefore, quality models always need to be adaptable to the respective project goals and contexts. A first step towards better support for selecting and adapting quality models can be seen in a classification of existing quality models, especially with respect to their suitability for different purposes and contexts. Such a classification of quality models can be applied to provide an integrated overview of the variety of quality models. This article presents the idea of so called comprehensive quality model landscapes (CQMLs), which provide a classification scheme for quality models and help to get an overview of existing quality models and their relationships. The article describes the usage goals for such landscapes, presents a classification scheme, presents the initial concept of such landscapes, illustrates the concept with selected examples, and sketches open questions and future work.

Jens Heidrich, Jürgen Münch

Many software development organizations still lack support for obtaining intellectual control over their software development processes and for determining the performance of their processes and the quality of the produced products. Systematic support for detecting and reacting to critical project states in order to achieve planned goals is usually missing. One means to institutionalize measurement on the basis of explicit models is the development and establishment of a so-called Software Project Control Center (SPCC) for systematic quality assurance and management support. An SPCC is comparable to a control room, which is a well known term in the mechanical production domain. Its tasks include collecting, in- terpreting, and visualizing measurement data in order to provide context-, purpose-, and role-oriented information for all stakeholders (e.g., project managers, quality assurance manager, developers) during the execution of a software development project. The article will present an overview of SPCC concepts, a concrete instantiation that supports goal-oriented data visualization (G-SPCC approach), and experiences from practical applications.

Jürgen Münch, Jens Heidrich

Online interpretation and visualization of project data are gaining increasing importance on the long road towards predictable and controllable software project execution. This paper sketches the Sprint I controlling approach for software development projects and gives first evaluation results. The approach is grounded on the usage of context-oriented cluster curves and integrated in the framework of software project control centers.

Jürgen Münch, Jens Heidrich

Using quantitative data from past projects for software project estimation requires context knowledge that characterizes its origin and indicates its applicability for future use. This article sketches the SPRINT I technique for project planning and controlling. The underlying prediction mechanism is based on the identification of similar past projects and the building of so-called clusters with typical data curves. The article focuses on how to characterize these clusters with context knowledge and how to use context information from actual projects for prediction. The SPRINT approach is tool-supported and first evaluations have been conducted.

Adam Trendowicz, Jens Heidrich, Jürgen Münch

Cost estimation is a very crucial field for software developing companies. In the context of learning organizations, estimation applicability and accuracy are not the only acceptance criteria. The contribution of an estimation technique to the understanding and maturing of related organizational processes (such as identification of cost and productivity factors, measurement, data validation, model validation, model maintenance) has recently been gaining increasing importance. Yet, most of the proposed cost modeling approaches provide software engineers with hardly any assistance in supporting related processes. Insufficient support is provided for validating created cost models (including underlying data collection processes) or, if valid models are obtained, for applying them to achieve an organization's objectives such as improved productivity or reduced schedule. This paper presents an enhancement of the CoBRA(R) cost modeling method by systematically including additional quantitative methods into iterative analysis-feedback cycles. Applied at Oki Electric Industry Co., Ltd., Japan, the CoBRA(R) method contributed to the achievement of the following objectives, including: (1) maturation of existing measurement processes, (2) increased expertise of Oki software project decision makers regarding cost-related software processes, and, finally, (3) reduction of initial estimation error from an initial 120% down to 14%.

Victor Basili, Jens Heidrich, Mikael Lindvall et al.

In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQM+Strategies(R) measurement approach that builds on the GQM approach to plan and implement software measurement. GQM+Strategies(R) provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. The proposed method is illustrated in the context of an example application of the method.

Jens Heidrich, Jürgen Münch

Software Cockpits, also known as Software Project Control Centers, support the management and controlling of software and system development projects and provide means for quantitative measurement-based project control. Currently, many companies are developing simple control dashboards that are mainly based on Spreadsheet applications. Alternatively, they use solutions providing a fixed set of project control functionality that cannot be sufficiently customized to their specific needs and goals. Specula is a systematic approach for defining reusable, customizable control components and instantiate them according to different organizational goals and characteristics based on the Quality Improvement Paradigm (QIP) and GQM. This article gives an overview of the Specula approach, including the basic conceptual model, goal-oriented measurement, and the composition of control components based on explicitly stated measurement goals. Related approaches are discussed and the use of Specula as part of industrial case studies is described.

Jens Heidrich, Adam Trendowicz, Jürgen Münch et al.

The increasing availability of cost-relevant data in industry allows companies to apply data-intensive estimation methods. However, available data are often inconsistent, invalid, or incomplete, so that most of the existing data-intensive estimation methods cannot be applied. Only few estimation methods can deal with imperfect data to a certain extent (e.g., Optimized Set Reduction, OSR(c)). Results from evaluating these methods in practical environments are rare. This article describes a case study on the application of OSR(c) at Toshiba Information Systems (Japan) Corporation. An important result of the case study is that estimation accuracy significantly varies with the data sets used and the way of preprocessing these data. The study supports current results in the area of quantitative cost estimation and clearly illustrates typical problems. Experiences, lessons learned, and recommendations with respect to data preprocessing and data-intensive cost estimation in general are presented.

Peter Liggesmeyer, Jens Heidrich, Jürgen Münch et al.

Many software development organizations still lack support for obtaining intellectual control over their software development processes and for determining the performance of their processes and the quality of the produced products. Systematic support for detecting and reacting to critical process and product states in order to achieve planned goals is usually missing. One means to institutionalize measurement on the basis of explicit models is the development and establishment of a so-called Software Project Control Center (SPCC) for systematic quality assurance and management support. An SPCC is comparable to a control room, which is a well known term in the mechanical production domain. One crucial task of an SPCC is the systematic visualization of measurement data in order to provide context-, purpose-, and role-oriented information for all stakeholders (e.g., project managers, quality assurance managers, developers) during the execution of a software development project. The article will present an overview of SPCC concepts, a concrete instantiation that supports goal-oriented data visualization, as well as examples and experiences from practical applications.

Jens Heidrich, Jürgen Münch

Setting up effective and efficient mechanisms for controlling software and system development projects is still challenging in industrial practice. On the one hand, necessary prerequisites such as established development processes, understanding of cause-effect relationships on relevant indicators, and sufficient sustainability of measurement programs are often missing. On the other hand, there are more fundamental methodological deficits related to the controlling process itself and to appropriate tool support. Additional activities that would guarantee the usefulness, completeness, and precision of the result- ing controlling data are widely missing. This article presents a conceptual architecture for so-called Software Project Control Centers (SPCC) that addresses these challenges. The architecture includes mechanisms for getting sufficiently precise and complete data and supporting the information needs of different stakeholders. In addition, an implementation of this architecture, the so-called Specula Project Support Environment, is sketched, and results from evaluating this implementation in industrial settings are presented.

Martin Kowalczyk, Henning Barthel, Jürgen Münch et al.

Explicitly linking software-related activities to an organisation's higher-level goals has been shown to be critical for organizational success. GQM+Strategies provides mechanisms for explicitly linking goals and strategies, based on goal-oriented strategic measurement systems. Deploying such strategic measurement systems in an organization is highly challenging. Experience has shown that a clear deployment strategy is needed for achieving sustainable success. In particular, an adequate deployment process as well as corresponding tool support can facilitate the deployment. This paper introduces the systematical GQM+Strategies deployment process and gives an overview of GQM+Strategies modelling and associated tool support. Additionally, it provides an overview of industrial applications and describes success factors and benefits for the usage of GQM+Strategies.

Victor R. Basili, Jens Heidrich, Mikael Lindvall et al.

Most of today's products and services are software-based. Organizations that develop software want to maintain and improve their competitiveness by controlling software-related risks. To do this, they need to align their business goals with software development strategies and translate them into quantitative project management. There is also an increasing need to justify cost and resources for software and system development and other IT services by demonstrating their impact on an organisation's higher-level goals. For both, linking business goals and software-related efforts in an organization is necessary. However, this is a challenging task, and there is a lack of methods addressing this gap. The GQM+Strategies approach effectively links goals and strategies on all levels of an organization by means of goal-oriented measurement. The approach is based on rationales for deciding about options when operationalizing goals and for evaluating the success of strategies with respect to goals.