Analytics for Software Project Management -- Where are We and Where do We Go?

Nayebi, Maleknaz; Ruhe, Guenther; Mota, Roberta Cabral; Mufti, Mujeeb

doi:10.1109/asew.2015.28

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…We mitigated this risk by careful screening of the participants and their background. 35 Fig. 12: The logged time when the escalation is done manually versus escalation using ESSMArT.…”

Section: Limitations and Threats To Validitymentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

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Quality and market acceptance of software products is strongly influenced by responsiveness to user requests. Once a request is received from a customer, decisions need to be made if the request should be escalated to the development team. Once escalated, the ticket must be formulated as a development task and be assigned to a developer. To make the process more efficient and reduce the time between receiving and escalating the user request, we aim to automate of the complete user request management process. We propose a holistic method called ESSMArT. The methods performs text summarization, predicts ticket escalation, creates the title and content of the ticket used by developers, and assigns the ticket to an available developer. We internally evaluated the method by 4,114 user tickets from Brightsquid and their secure health care communication platform Secure-Mail. We also perform an external evaluation on the usefulness of the approach. We found that supervised learning based on context specific data performs best for extractive summarization. For predicting escalation of tickets, Random Forest trained on a combination of conversation and extractive summarization is best with highest precision (of 0.9) and recall (of 0.55). From external evaluation we found that ESSMArT provides suggestions that are 71% aligned with human ones. Applying the prototype implementation to 315 user requests

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“…We mitigated this risk by careful screening of the participants and their background. 35 Fig. 12: The logged time when the escalation is done manually versus escalation using ESSMArT.…”

Section: Limitations and Threats To Validitymentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

Self Cite

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“…Properly addressing customer concerns is critical in the highly competitive and dynamic environment. In the context of mobile app stores, we study the ARP process to balance stakeholders' satisfaction and dissatisfaction for proposing (new) mobile apps [45] [48]. For that purpose, we looked at Over-The-Top (OTT) TV services [29] apps offered in the Android app store market and plan for the next release (K = 1) of that product.…”

Section: Rq3: Evaluationmentioning

confidence: 99%

“…Besides comparing the results of SDO with other algorithms, we have performed an external evaluation from running two rounds of surveys with actual stakeholders to make sure about the relevance of the analysis with managers' Algorithm 3 Two factor greedy heuristic Total Effort + = Effort (f(n)); end if while (Total Effort < Capacity) end for need [49]. Industrial evaluation of release planning models is known to be inherently difficult.…”

Section: Expert Evaluation Of Plansmentioning

confidence: 99%

Asymmetric Release Planning: Compromising Satisfaction against Dissatisfaction

Nayebi

Ruhe

2019

IIEEE Trans. Software Eng.

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Maximizing satisfaction from offering features as part of the upcoming release(s) is different from minimizing dissatisfaction gained from not offering features. This asymmetric behavior has never been utilized for product release planning. We study Asymmetric Release Planning (ARP) by accommodating asymmetric feature evaluation. We formulated and solved ARP as a bicriteria optimization problem. In its essence, it is the search for optimized trade-offs between maximum stakeholder satisfaction and minimum dissatisfaction. Different techniques including a continuous variant of Kano analysis are available to predict the impact on satisfaction and dissatisfaction with a product release from offering or not offering a feature. As a proof of concept, we validated the proposed solution approach called Satisfaction-Dissatisfaction Optimizer (SDO) via a real-world case study project. From running three replications with varying effort capacities, we demonstrate that SDO generates optimized trade-off solutions being (i) of a different value profile and different structure, (ii) superior to the application of random search and heuristics in terms of quality and completeness, and (iii) superior to the usage of manually generated solutions generated from managers of the case study company. A survey with 20 stakeholders evaluated the applicability and usefulness of the generated results.

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“…In both scenarios, the analysis is concerned with the sequence of events that are happening in the project and how these sequences relate to the specified outcomes. The managers can make their own model from the system and analyze the problems [36] by adopting some proposed models for code review practices and inter-team coordination [5] in Scenario #1 and branching strategies for scenario #2 [44]. However, having a semi-automatic method such as GWM makes obtaining such experiences less time consuming and more accurate to perform.…”

Section: Treatmentsmentioning

confidence: 99%

Mining Treatment-Outcome Constructs from Sequential Software Engineering Data

Nayebi

Ruhe

Zimmermann

2021

IIEEE Trans. Software Eng.

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Many investigations in empirical software engineering look at sequences of data resulting from development or management processes. In this paper, we propose an analytical approach called the Gandhi-Washington Method (GWM) to investigate the impact of recurring events in software projects. GWM takes an encoding of events and activities provided by a software analyst as input. It uses regular expressions to automatically condense and summarize information and infer treatments. Relating the treatments to the outcome through statistical tests, treatment-outcome constructs are automatically mined from the data. The output of GWM is a set of treatment-outcome constructs. Each treatment in the set of mined constructs is significantly different from the other treatments considering the impact on the outcome and/or is structurally different from other treatments considering the sequence of events. We describe GWM and classes of problems to which GWM can be applied. We demonstrate the applicability of this method for empirical studies on sequences of file editing, code ownership, and release cycle time.Scenario #1: Project manager Alice is looking into ways to enhance the healthiness of the code developed by her team. The team doesn't have a unified way of committing, testing and reviewing the code. Some of the sub-project teams follow a commit-test-review process, while others apply review-test-commit. The rest does not follow either of these strategies to review their code. Alice wonders if the selection of one of these strategies affects the number of code bugs. To answer this question, she selects GWM and encodes each method for testing and reviewing a file by the sequence of testing (T), committing (C), and reviewing Experiment design

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Analytics for Software Project Management -- Where are We and Where do We Go?

Cited by 16 publications

References 5 publications

ESSMArT way to manage customer requests

ESSMArT way to manage customer requests

Asymmetric Release Planning: Compromising Satisfaction against Dissatisfaction

Mining Treatment-Outcome Constructs from Sequential Software Engineering Data

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