Evaluation and Measurement of Software Process Improvement -- A Systematic Literature Review

BACKGROUND: Software Process Improvement (SPI) is a systematic approach to increase the efficiency and effectiveness of a software development organization and to enhance software products. OBJECTIVE: This paper aims to identify and characterize evaluation strategies and measurements used to assess the impact of different SPI initiatives. METHOD: The systematic literature review includes 148 papers published between 1991 and 2008. The selected papers were classified according to SPI initiative, applied evaluation strategies, and measurement perspectives. Potential confounding factors interfering with the evaluation of the improvement effort were assessed. RESULTS: Seven distinct evaluation strategies were identified, wherein the most common one, "Pre-Post Comparison" was applied in 49 percent of the inspected papers. Quality was the most measured attribute (62 percent), followed by Cost (41 percent), and Schedule (18 percent). Looking at measurement perspectives, "Project" represents the majority with 66 percent. CONCLUSION: The evaluation validity of SPI initiatives is challenged by the scarce consideration of potential confounding factors, particularly given that "Pre-Post Comparison" was identified as the most common evaluation strategy, and the inaccurate descriptions of the evaluation context. Measurements to assess the short and mid-term impact of SPI initiatives prevail, whereas long-term measurements in terms of customer satisfaction and return on investment tend to be less used

A conceptual framework for SPI evaluation

Software Process Improvement (SPI) encompasses the analysis and modification of the processes within software development, aimed at improving key areas that contribute to the organizations' goals. The task of evaluating whether the selected improvement path meets these goals is challenging. On the basis of the results of a systematic literature review on SPI measurement and evaluation practices, we developed a framework (SPI Measurement and Evaluation Framework (SPI-MEF)) that supports the planning and implementation of SPI evaluations. SPI-MEF guides the practitioner in scoping the evaluation, determining measures, and performing the assessment. SPI-MEF does not assume a specific approach to process improvement and can be integrated in existing measurement programs, refocusing the assessment on evaluating the improvement initiative's outcome. Sixteen industry and academic experts evaluated the framework's usability and capability to support practitioners, providing additional insights that were integrated in the application guidelines of the framework

Software Startups -- A Research Agenda

Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs

Exploration of technical debt in start-ups

Abstract 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

A progression model of software engineering goals, challenges, and practices in start-ups

Abstract Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start- ups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors

Use of agile practices in start-up companies

Abstract Context: Software start-ups have shown their ability to develop and launch innovative software products and services. Small, motivated teams and uncertain project scope makes start-ups good candidates for adopting Agile practices. Objective: We explore how start-ups use Agile practices and what effects can be associated with the use of those practices. Method: We use a case survey to analyze 84 start-up cases and 56 Agile practices. We apply statistical methods to test for statistically significant associations between the use of Agile practices, team, and product factors. Results: Our results suggest that development of the backlog, use of version control, code refactoring, and development of user stories are the most frequently reported practices. We identify 22 associations between the use of Agile practices, team, and product factors. The use of Agile practices is associated with effects on source code and overall product quality. A teams’ positive or negative attitude towards best engineering practices is a significant indicator for either adoption or rejection of certain Agile practices. To explore the relationships in our findings, we set forth a number of propositions that can be investigated in future research. Conclusions: We conclude that start-ups use Agile practices, however without following any specific methodology. We identify the opportunity for more fine-grained studies into the adoption and effects of individual Agile practices. Start-up practitioners could benefit from Agile practices in terms of better overall quality, tighter control over team performance, and resource utilization

An anatomy of requirements engineering in software startups using multi-vocal literature and case survey

Abstract Context: Software startups aim to develop innovative products, grow rapidly, and thus become important in the development of economy and jobs. Requirements engineering (RE) is a key process area in software development, but its effects on software startups are unclear. Objective: The main objective of this study was to explore how RE (elicitation, documentation, prioritization and validation) is used in software startups. Method: A multi-vocal literature review (MLR) was used to find scientific and gray literature. In addition, a case survey was employed to gather empirical data to reach this study’s objective. Results: In the MLR, 36 primary articles were selected out of 28,643 articles. In the case survey, 80 respondents provided information about software startup cases across the globe. Data analysis revealed that during RE processes, internal sources (e.g., for source), analyses of similar products (e.g., elicitation), uses of informal notes (e.g., for documentation), values to customers, products and stakeholders (e.g., for prioritization) and internal reviews/prototypes (e.g., for validation) were the most used techniques. Conclusion: After an analysis of primary literature, it was concluded that research on this topic is still in early stages and more systematic research is needed. Furthermore, few topics were suggested for future research