@article {Idri2015, title = {Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques}, journal = {International Journal of Intelligent Systems}, year = {2015}, note = {cited By 1; Article in Press}, abstract = {Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy. {\textcopyright} 2015 Wiley Periodicals, Inc.}, doi = {10.1002/int.21748}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938886103\&doi=10.1002\%2fint.21748\&partnerID=40\&md5=d6d3bf98e8678fff671b7bc18b1febd5}, author = {Idri, A.a and Amazal, F.A.a and Abran, A.b} } @article {Idri2015206, title = {Analogy-based software development effort estimation: A systematic mapping and review}, journal = {Information and Software Technology}, volume = {58}, year = {2015}, note = {cited By 16}, pages = {206-230}, abstract = {Context: Analogy-based Software development Effort Estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, existing systematic map and review studies on software development effort prediction have not investigated in depth several issues of ASEE techniques, to the exception of comparisons with other types of estimation techniques. Objective: The objective of this research is twofold: (1) to classify ASEE studies which primary goal is to propose new or modified ASEE techniques according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools. Method: We performed a systematic mapping of studies for which the primary goal is to develop or to improve ASEE techniques published in the period 1990-2012, and reviewed them based on an automated search of four electronic databases. Results: In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with Fuzzy Logic (FL) or Genetic Algorithms (GA). Conclusion: Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry. {\textcopyright} 2014 Elsevier B.V. All rights reserved.}, doi = {10.1016/j.infsof.2014.07.013}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914109039\&doi=10.1016\%2fj.infsof.2014.07.013\&partnerID=40\&md5=70ffdeca252a6c01dde0070da6ecde9c}, author = {Idri, A.a and Amazal, F.A.a and Abran, A.b} } @article {Idri2014, title = {Analogy-based software development effort estimation: A systematic mapping and review}, journal = {Information and Software Technology}, year = {2014}, note = {cited By 1; Article in Press}, abstract = {Context: Analogy-based software development effort estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, to our knowledge, no systematic mapping has been created of ASEE studies and no review has been carried out to analyze the empirical evidence on the performance of ASEE techniques. Objective: The objective of this research is twofold: (1) to classify ASEE papers according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends; and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools. Method: We performed a systematic mapping of ASEE studies published in the period 1990-2012, and reviewed them based on an automated search of four electronic databases. Results: In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with fuzzy logic (FL) or genetic algorithms (GA). Conclusion: Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry. {\textcopyright} 2014 Elsevier B.V. All rights reserved.}, doi = {10.1016/j.infsof.2014.07.013}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906037417\&doi=10.1016\%2fj.infsof.2014.07.013\&partnerID=40\&md5=4a5eda69bfe8b11111bed58fad572629}, author = {Idri, A.a and Amazal, F.A.a and Abran, A.b} } @conference {Amazal2014247, title = {Improving fuzzy analogy based software development effort estimation}, booktitle = {Proceedings - Asia-Pacific Software Engineering Conference, APSEC}, volume = {1}, year = {2014}, note = {cited By 0}, pages = {247-254}, abstract = {Analogy-based estimation has recently emerged as a promising technique and a viable alternative to other conventional estimation methods. One of the most important research areas for analogy-based cost estimation is how to predict the effort of software projects when they are described by mixed numerical and categorical data. To address this issue, we have proposed, in an earlier work, a new approach called fuzzy analogy combining the key features of fuzzy logic and analogybased reasoning. However, fuzzy analogy may only be used when the possible values of the categorical attributes are derived from a numerical domain. The current study aims to extend our former approach to correctly handle categorical data. To this end, the fuzzy k-modes algorithm is used with two initialization techniques. The performance of the proposed approach was compared with that of classical analogy using the International Software Benchmarking Standards Group (ISBSG) dataset. The obtained results show significant improvement in estimation accuracy. {\textcopyright} 2014 IEEE.}, doi = {10.1109/APSEC.2014.46}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951282652\&doi=10.1109\%2fAPSEC.2014.46\&partnerID=40\&md5=864ca37db60e0c0338a8f4ae557a43a4}, author = {Amazal, F.A.a and Idri, A.a and Abran, A.b} } @article {Amazal2014, title = {Software development effort estimation using classical and fuzzy analogy: A cross-validation comparative study}, journal = {International Journal of Computational Intelligence and Applications}, volume = {13}, number = {3}, year = {2014}, note = {cited By 4}, abstract = {Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models. {\textcopyright} Imperial College Press.}, doi = {10.1142/S1469026814500138}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908567208\&doi=10.1142\%2fS1469026814500138\&partnerID=40\&md5=7cc9c1aeaf80c1739df9a20546ac1885}, author = {Amazal, F.A.a and Idri, A.a and Abran, A.b} }