Context: Software effort estimation is a very important component of the software development life cycle. It underpins activities such as planning, maintenance and bidding. Therefore, it has triggered much research over the past four decades, including many machine learning approaches. One popular approach, that has the benefit of accessible reasoning, is analogy-based estimation. Machine learning including analogy is known to significantly benefit from feature selection/weighting. Unfortunately feature weighting search is an NP hard problem, therefore computationally very demanding, if not intractable. Objective: Therefore, one objective of this research is to develop an effi cient and effective feature weighting algorithm for estimation by analogy. However, a major challenge for the effort estimation research community is that experimental results tend to be contradictory and also lack reliability. This has been paralleled by a recent awareness of how bias can impact research results. This is a contributory reason why software effort estimation is still an open problem. Consequently the second objective is to investigate research methods that might lead to more reliable results and focus on blinding methods to reduce researcher bias. Method: In order to build on the most promising feature weighting algorithms I conduct a systematic literature review. From this I develop a novel and e fficient feature weighting algorithm. This is experimentally evaluated, comparing three feature weighting approaches with a na ive benchmark using 2 industrial data sets. Using these experiments, I explore blind analysis as a technique to reduce bias. Results: The systematic literature review conducted identified 19 relevant primary studies. Results from the meta-analysis of selected studies using a one-sample sign test (p = 0.0003) shows a positive effect - to feature weighting in general compared with ordinary analogy-based estimation (ABE), that is, feature weighting is a worthwhile technique to improve ABE. Nevertheless the results remain imperfect so there is still much scope for improvement. My experience shows that blinding can be a relatively straightforward procedure. I also highlight various statistical analysis decisions which ought not be guided by the hunt for statistical significance and show that results can be inverted merely through a seemingly inconsequential statistical nicety. After analysing results from 483 software projects from two separate industrial data sets, I conclude that the proposed technique improves accuracy over the standard feature subset selection (FSS) and traditional case-based reasoning (CBR) when using pseudo time-series validation. Interestingly, there is no strong evidence for superior performance of the new technique when traditional validation techniques (jackknifing) are used but is more effi cient. Conclusion: There are two main findings: (i) Feature weighting techniques are promising for software effort estimation but they need to be tailored for target case for their potential to be adequately exploited. Despite the research findings showing that assuming weights differ in different parts of the instance space ('local' regions) may improve effort estimation results - majority of studies in software effort estimation (SEE) do not take this into consideration. This represents an improvement on other methods that do not take this into consideration. (ii) Whilst there are minor challenges and some limits to the degree of blinding possible, blind analysis is a very practical and an easy-to-implement method that supports more objective analysis of experimental results. Therefore I argue that blind analysis should be the norm for analysing software engineering experiments.

Software effort estimation still remains a challenging and debatable research area. Most of the software effort estimation models take software size as the base input. Among the others, Constructive Cost Model (COCOMO II) is a widely known effort estimation model. It uses Source Lines of Code (SLOC) as the software size to estimate effort. However, many problems arise while using SLOC as a size measure due to its late availability in the software life cycle. Therefore, a lot of research has been going on to identify the nature of relationship between software functional size and effort since functional size can be measured very early when the functional user requirements are available. There are many other project related factors that were found to be affecting the effort estimation based on software size. Application Type, Programming Language, Development Type are some of them. This thesis aims to investigate the nature of relationship between software size and development effort. It explains known effort estimation models and gives an understanding about the Function Point and Functional Size Measurement (FSM) method. Factors, affecting relationship between software size and development effort, are also identified. In the end, an effort estimation model is developed after statistical analyses. We present the results of an empirical study which we conducted to investigate the significance of different project related factors on the relationship between functional size and effort. We used the projects data in the International Software Benchmarking Standards Group (ISBSG) dataset. We selected the projects which were measured by utilizing the Common Software Measurement International Consortium (COSMIC) Function Points. For statistical analyses, we performed step wise Analysis of Variance (ANOVA) and Analysis of Co-Variance (ANCOVA) techniques to build the multi variable models. We also performed Multiple Regression Analysis to formalize the relation. / Software effort estimation still remains a challenging and debatable research area. Most of the software effort estimation models take software size as the base input. Among the others, Constructive Cost Model (COCOMO II) is a widely known effort estimation model. It uses Source Lines of Code (SLOC) as the software size to estimate effort. However, many problems arise while using SLOC as a size measure due to its late availability in the software life cycle. Therefore, a lot of research has been going on to identify the nature of relationship between software functional size and effort since functional size can be measured very early when the functional user requirements are available. There are many other project related factors that were found to be affecting the effort estimation based on software size. Application Type, Programming Language, Development Type are some of them. This thesis aims to investigate the nature of relationship between software size and development effort. It explains known effort estimation models and gives an understanding about the Function Point and Functional Size Measurement (FSM) method. Factors, affecting relationship between software size and development effort, are also identified. In the end, an effort estimation model is developed after statistical analyses. We present the results of an empirical study which we conducted to investigate the significance of different project related factors on the relationship between functional size and effort. We used the projects data in the International Software Benchmarking Standards Group (ISBSG) dataset. We selected the projects which were measured by utilizing the Common Software Measurement International Consortium (COSMIC) Function Points. For statistical analyses, we performed step wise Analysis of Variance (ANOVA) and Analysis of Co-Variance (ANCOVA) techniques to build the multi variable models. We also performed Multiple Regression Analysis to formalize the relation. / +46-(0)-739763245